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449943263175424
[ { "type": "text", "value": "next version of ", "raw": "next version of ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/sequelbox/Celestia", "resource": { "type": "dataset", "id": "sequelbox/Celestia", "discussionNum": null }, "url": "https://huggingface.co/datasets/sequelbox/Celestia", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " will be ", "raw": " will be ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1", "resource": { "type": "dataset", "id": "microsoft/orca-agentinstruct-1M-v1", "discussionNum": null }, "url": "https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " style. coming soon", "raw": " style. coming soon", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
next version of https://huggingface.co/datasets/sequelbox/Celestia will be https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1 style. coming soon
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2024-11-17T18:26:14.000Z
2024-11-17T18:26:14.695Z
[]
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"https://huggingface.co/prithivMLmods/Flux.1-Dev-Ctoon-LoRA", "resource": { "type": "model", "id": "prithivMLmods/Flux.1-Dev-Ctoon-LoRA", "discussionNum": null }, "url": "https://huggingface.co/prithivMLmods/Flux.1-Dev-Ctoon-LoRA", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€Collection:", "raw": "๐Ÿš€Collection:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "{ Flux LoRA Collection} : ", 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"https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32", "resource": { "type": "collection", "id": "prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32", "discussionNum": null }, "url": "https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€For More Visit", "raw": "๐Ÿš€For More Visit", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/strangerzonehf", "resource": null, "url": null, "href": "https://huggingface.co/strangerzonehf", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": 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Minimalistic Adapters ๐ŸŽƒ ๐Ÿš€Demo Here: https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC ๐Ÿš€Model: { Quote Tuner } : https://huggingface.co/prithivMLmods/Flux.1-Dev-Quote-LoRA { Stamp Art } : https://huggingface.co/prithivMLmods/Flux.1-Dev-Stamp-Art-LoRA { Hand Sticky } : https://huggingface.co/prithivMLmods/Flux.1-Dev-Hand-Sticky-LoRA { Poster HQ } : https://huggingface.co/prithivMLmods/Flux.1-Dev-Poster-HQ-LoRA { Ctoon Min } : https://huggingface.co/prithivMLmods/Flux.1-Dev-Ctoon-LoRA ๐Ÿš€Collection: { Flux LoRA Collection} : https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be { LoRA Space Collection } : https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32 ๐Ÿš€For More Visit https://huggingface.co/strangerzonehf . . . ๐Ÿค—@prithivMLmods
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2024-11-17T17:14:32.000Z
2024-11-17T18:22:17.255Z
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[ { "type": "text", "value": "Kohya brought massive improvements to FLUX LoRA (as low as 4 GB GPUs) and DreamBooth / Fine-Tuning (as low as 6 GB GPUs) training - check attached images in full size to see full details", "raw": "Kohya brought massive improvements to FLUX LoRA (as low as 4 GB GPUs) and DreamBooth / Fine-Tuning (as low as 6 GB GPUs) training - check attached images in full size to see full details", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "You can download all configs and full instructions", "raw": "You can download all configs and full instructions", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> ", "raw": "> ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/112099700", "resource": null, "url": null, "href": "https://www.patreon.com/posts/112099700", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " - Fine Tuning post", "raw": " - Fine Tuning post", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> ", "raw": "> ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/110879657", "resource": null, "url": null, "href": "https://www.patreon.com/posts/110879657", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " - LoRA post", "raw": " - LoRA post", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Kohya brought massive improvements to FLUX LoRA and DreamBooth / Fine-Tuning (min 6GB GPU) training.", "raw": "Kohya brought massive improvements to FLUX LoRA and DreamBooth / Fine-Tuning (min 6GB GPU) training.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Now as low as 4GB GPUs can train FLUX LoRA with decent quality and 24GB and below GPUs got a huge speed boost when doing Full DreamBooth / Fine-Tuning training", "raw": "Now as low as 4GB GPUs can train FLUX LoRA with decent quality and 24GB and below GPUs got a huge speed boost when doing Full DreamBooth / Fine-Tuning training", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "You need minimum 4GB GPU to do a FLUX LoRA training and minimum 6 GB GPU to do FLUX DreamBooth / Full Fine-Tuning training. It is just mind blowing.", "raw": "You need minimum 4GB GPU to do a FLUX LoRA training and minimum 6 GB GPU to do FLUX DreamBooth / Full Fine-Tuning training. It is just mind blowing.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "You can download all configs and full instructions > ", "raw": "You can download all configs and full instructions > ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/112099700", "resource": null, "url": null, "href": "https://www.patreon.com/posts/112099700", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The above post also has 1-click installers and downloaders for Windows, RunPod and Massed Compute", "raw": "The above post also has 1-click installers and downloaders for Windows, RunPod and Massed Compute", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The model downloader scripts also updated and downloading 30+GB models takes total 1 minute on Massed Compute", "raw": "The model downloader scripts also updated and downloading 30+GB models takes total 1 minute on Massed Compute", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "You can read the recent updates here : ", "raw": "You can read the recent updates here : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/kohya-ss/sd-scripts/tree/sd3?tab=readme-ov-file#recent-updates", "resource": null, "url": null, "href": "https://github.com/kohya-ss/sd-scripts/tree/sd3?tab=readme-ov-file#recent-updates", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This is the Kohya GUI branch : ", "raw": "This is the Kohya GUI branch : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/bmaltais/kohya_ss/tree/sd3-flux.1", "resource": null, "url": null, "href": "https://github.com/bmaltais/kohya_ss/tree/sd3-flux.1", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Key thing to reduce VRAM usage is using block swap", "raw": "Key thing to reduce VRAM usage is using block swap", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Kohya implemented the logic of OneTrainer to improve block swapping speed significantly and now it is supported for LoRAs as well", "raw": "Kohya implemented the logic of OneTrainer to improve block swapping speed significantly and now it is supported for LoRAs as well", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Now you can do FP16 training with LoRAs on 24 GB and below GPUs", "raw": "Now you can do FP16 training with LoRAs on 24 GB and below GPUs", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Now you can train a FLUX LoRA on a 4 GB GPU - key is FP8, block swap and using certain layers training (remember single layer LoRA training)", "raw": "Now you can train a FLUX LoRA on a 4 GB GPU - key is FP8, block swap and using certain layers training (remember single layer LoRA training)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It took me more than 1 day to test all newer configs, their VRAM demands, their relative step speeds and prepare the configs :)", "raw": "It took me more than 1 day to test all newer configs, their VRAM demands, their relative step speeds and prepare the configs :)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Kohya brought massive improvements to FLUX LoRA (as low as 4 GB GPUs) and DreamBooth / Fine-Tuning (as low as 6 GB GPUs) training - check attached images in full size to see full details You can download all configs and full instructions > https://www.patreon.com/posts/112099700 - Fine Tuning post > https://www.patreon.com/posts/110879657 - LoRA post Kohya brought massive improvements to FLUX LoRA and DreamBooth / Fine-Tuning (min 6GB GPU) training. Now as low as 4GB GPUs can train FLUX LoRA with decent quality and 24GB and below GPUs got a huge speed boost when doing Full DreamBooth / Fine-Tuning training You need minimum 4GB GPU to do a FLUX LoRA training and minimum 6 GB GPU to do FLUX DreamBooth / Full Fine-Tuning training. It is just mind blowing. You can download all configs and full instructions > https://www.patreon.com/posts/112099700 The above post also has 1-click installers and downloaders for Windows, RunPod and Massed Compute The model downloader scripts also updated and downloading 30+GB models takes total 1 minute on Massed Compute You can read the recent updates here : https://github.com/kohya-ss/sd-scripts/tree/sd3?tab=readme-ov-file#recent-updates This is the Kohya GUI branch : https://github.com/bmaltais/kohya_ss/tree/sd3-flux.1 Key thing to reduce VRAM usage is using block swap Kohya implemented the logic of OneTrainer to improve block swapping speed significantly and now it is supported for LoRAs as well Now you can do FP16 training with LoRAs on 24 GB and below GPUs Now you can train a FLUX LoRA on a 4 GB GPU - key is FP8, block swap and using certain layers training (remember single layer LoRA training) It took me more than 1 day to test all newer configs, their VRAM demands, their relative step speeds and prepare the configs :)
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2024-11-17T14:49:39.000Z
2024-11-17T14:49:39.775Z
[]
/posts/MonsterMMORPG/982072243005650
263
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[ { "type": "text", "value": "Ok RNNs can rap too:)", "raw": "Ok RNNs can rap too:)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Here we implement the seminal RNN paper โ€œGenerating Text with Recurrent Neural Networks\"- we train a character-level multiplicative recurrent neural network model (~250k params) for 1000 epochs with Adam opt onย 2pac's \"Hit 'em Up\", sample was fun lol.", "raw": "Here we implement the seminal RNN paper โ€œGenerating Text with Recurrent Neural Networks\"- we train a character-level multiplicative recurrent neural network model (~250k params) for 1000 epochs with Adam opt onย 2pac's \"Hit 'em Up\", sample was fun lol.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Code: ", "raw": "Code: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/Jaykef/ai-algorithms/blob/main/generating_texts_with_rnns.ipynb", "resource": null, "url": null, "href": "https://github.com/Jaykef/ai-algorithms/blob/main/generating_texts_with_rnns.ipynb", "user": null, "lang": null, "code": null, "label": null } ]
Ok RNNs can rap too:) Here we implement the seminal RNN paper โ€œGenerating Text with Recurrent Neural Networks"- we train a character-level multiplicative recurrent neural network model (~250k params) for 1000 epochs with Adam opt onย 2pac's "Hit 'em Up", sample was fun lol. Code: https://github.com/Jaykef/ai-algorithms/blob/main/generating_texts_with_rnns.ipynb
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2024-11-17T13:02:24.000Z
2024-11-17T13:02:24.594Z
[]
/posts/Jaward/953449438611686
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611948696998118
[ { "type": "text", "value": "Finaly I realesed mediapipe-face animation space.", "raw": "Finaly I realesed mediapipe-face animation space.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Mediapipe 68-points Eyes-Closed and Mouth-Opened", "raw": "Mediapipe 68-points Eyes-Closed and Mouth-Opened", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Akjava/mediapipe-68-facial-guide-eyes-closed-mouth-opened", "resource": { "type": "space", "id": "Akjava/mediapipe-68-facial-guide-eyes-closed-mouth-opened", "discussionNum": null }, "url": "https://huggingface.co/spaces/Akjava/mediapipe-68-facial-guide-eyes-closed-mouth-opened", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "[Article]Results: Converted Guide Images(eyes-closed and mouth-opened) with Flux.1 schenll img2img/inpaint", "raw": "[Article]Results: Converted Guide Images(eyes-closed and mouth-opened) with Flux.1 schenll img2img/inpaint", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/Akjava/result-guide-image-eyes-mouth", "resource": null, "url": null, "href": "https://huggingface.co/blog/Akjava/result-guide-image-eyes-mouth", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "All the other tools listed are designed to support Mediapipe Face Animation", "raw": "All the other tools listed are designed to support Mediapipe Face Animation", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collections/Akjava/mediapipe-tools-672ffe8ee7b62763c31b70c7", "resource": null, "url": null, "href": "https://huggingface.co/collections/Akjava/mediapipe-tools-672ffe8ee7b62763c31b70c7", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collections/Akjava/webp-3-frame-talking-animation-tools-672819ce4989f354cdbcc739", "resource": null, "url": null, "href": "https://huggingface.co/collections/Akjava/webp-3-frame-talking-animation-tools-672819ce4989f354cdbcc739", "user": null, "lang": null, "code": null, "label": null } ]
Finaly I realesed mediapipe-face animation space. Mediapipe 68-points Eyes-Closed and Mouth-Opened https://huggingface.co/spaces/Akjava/mediapipe-68-facial-guide-eyes-closed-mouth-opened [Article]Results: Converted Guide Images(eyes-closed and mouth-opened) with Flux.1 schenll img2img/inpaint https://huggingface.co/blog/Akjava/result-guide-image-eyes-mouth All the other tools listed are designed to support Mediapipe Face Animation https://huggingface.co/collections/Akjava/mediapipe-tools-672ffe8ee7b62763c31b70c7 https://huggingface.co/collections/Akjava/webp-3-frame-talking-animation-tools-672819ce4989f354cdbcc739
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2024-11-17T12:43:13.000Z
2024-11-17T12:43:13.043Z
[]
/posts/Akjava/611948696998118
144
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478756824597278
[ { "type": "text", "value": "Good folks at ", "raw": "Good folks at ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@nvidia", "resource": null, "url": null, "href": null, "user": "nvidia", "lang": null, "code": null, "label": null }, { "type": "text", "value": " and @Tsinghua_Uni have released LLAMA-MESH - A Revolutionary Approach to 3D Content Generation!", "raw": " and @Tsinghua_Uni have released LLAMA-MESH - A Revolutionary Approach to 3D Content Generation!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This innovative framework enables the direct generation of 3D meshes from natural language prompts while maintaining strong language capabilities.", "raw": "This innovative framework enables the direct generation of 3D meshes from natural language prompts while maintaining strong language capabilities.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Here is the Architecture & Implementation!", "raw": "Here is the Architecture & Implementation!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ">> Core Components", "raw": ">> Core Components", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Model Foundation ", "raw": "Model Foundation ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- If you haven't guessed it yet, it's built on the LLaMA-3.1-8B-Instruct base model ", "raw": "- If you haven't guessed it yet, it's built on the LLaMA-3.1-8B-Instruct base model ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Maintains original language capabilities while adding 3D generation ", "raw": "- Maintains original language capabilities while adding 3D generation ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Context length is set to 8,000 tokens ", "raw": "- Context length is set to 8,000 tokens ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "3D Representation Strategy ", "raw": "3D Representation Strategy ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Uses the OBJ file format for mesh representation ", "raw": "- Uses the OBJ file format for mesh representation ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Quantizes vertex coordinates into 64 discrete bins per axis ", "raw": "- Quantizes vertex coordinates into 64 discrete bins per axis ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Sorts vertices by z-y-x coordinates, from lowest to highest ", "raw": "- Sorts vertices by z-y-x coordinates, from lowest to highest ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Sorts faces by the lowest vertex indices for consistency ", "raw": "- Sorts faces by the lowest vertex indices for consistency ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Data Processing Pipeline ", "raw": "Data Processing Pipeline ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Filters meshes to a maximum of 500 faces for computational efficiency ", "raw": "- Filters meshes to a maximum of 500 faces for computational efficiency ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Applies random rotations (0ยฐ, 90ยฐ, 180ยฐ, 270ยฐ) for data augmentation ", "raw": "- Applies random rotations (0ยฐ, 90ยฐ, 180ยฐ, 270ยฐ) for data augmentation ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Generates ~125k mesh variations from 31k base meshes ", "raw": "- Generates ~125k mesh variations from 31k base meshes ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Uses Cap3D-generated captions for text descriptions ", "raw": "- Uses Cap3D-generated captions for text descriptions ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ">> Training Framework", "raw": ">> Training Framework", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Dataset Composition ", "raw": "Dataset Composition ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 40% Mesh Generation tasks ", "raw": "- 40% Mesh Generation tasks ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 20% Mesh Understanding tasks ", "raw": "- 20% Mesh Understanding tasks ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 40% General Conversation (UltraChat dataset) ", "raw": "- 40% General Conversation (UltraChat dataset) ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 8x training turns for generation, 4x for understanding ", "raw": "- 8x training turns for generation, 4x for understanding ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Training Configuration ", "raw": "Training Configuration ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Deployed on 32 A100 GPUs (for Nvidia, this is literally in-house) ", "raw": "- Deployed on 32 A100 GPUs (for Nvidia, this is literally in-house) ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 21,000 training iterations ", "raw": "- 21,000 training iterations ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Global batch size: 128 ", "raw": "- Global batch size: 128 ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- AdamW optimizer with a 1e-5 learning rate ", "raw": "- AdamW optimizer with a 1e-5 learning rate ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 30-step warmup with cosine scheduling ", "raw": "- 30-step warmup with cosine scheduling ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Total training time: approximately 3 days (based on the paper) ", "raw": "- Total training time: approximately 3 days (based on the paper) ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This research opens exciting possibilities for intuitive 3D content creation through natural language interaction. The future of digital design is conversational!", "raw": "This research opens exciting possibilities for intuitive 3D content creation through natural language interaction. The future of digital design is conversational!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Good folks at @nvidia and @Tsinghua_Uni have released LLAMA-MESH - A Revolutionary Approach to 3D Content Generation! This innovative framework enables the direct generation of 3D meshes from natural language prompts while maintaining strong language capabilities. Here is the Architecture & Implementation! >> Core Components Model Foundation - If you haven't guessed it yet, it's built on the LLaMA-3.1-8B-Instruct base model - Maintains original language capabilities while adding 3D generation - Context length is set to 8,000 tokens 3D Representation Strategy - Uses the OBJ file format for mesh representation - Quantizes vertex coordinates into 64 discrete bins per axis - Sorts vertices by z-y-x coordinates, from lowest to highest - Sorts faces by the lowest vertex indices for consistency Data Processing Pipeline - Filters meshes to a maximum of 500 faces for computational efficiency - Applies random rotations (0ยฐ, 90ยฐ, 180ยฐ, 270ยฐ) for data augmentation - Generates ~125k mesh variations from 31k base meshes - Uses Cap3D-generated captions for text descriptions >> Training Framework Dataset Composition - 40% Mesh Generation tasks - 20% Mesh Understanding tasks - 40% General Conversation (UltraChat dataset) - 8x training turns for generation, 4x for understanding Training Configuration - Deployed on 32 A100 GPUs (for Nvidia, this is literally in-house) - 21,000 training iterations - Global batch size: 128 - AdamW optimizer with a 1e-5 learning rate - 30-step warmup with cosine scheduling - Total training time: approximately 3 days (based on the paper) This research opens exciting possibilities for intuitive 3D content creation through natural language interaction. The future of digital design is conversational!
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2024-11-17T07:57:31.000Z
2024-11-17T07:57:31.455Z
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/posts/singhsidhukuldeep/478756824597278
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OmniVision-968M: a new local VLM for edge devices, fast & small but performant ๐Ÿ’จ a new vision language model with 9x less image tokens, super efficient ๐Ÿ“– aligned with DPO for reducing hallucinations โšก๏ธ Apache 2.0 license ๐Ÿ”ฅ Demo hf.co/spaces/NexaAIDev/omnivlm-dpo-demo Model https://huggingface.co/NexaAIDev/omnivision-968M
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2024-11-16T23:26:19.000Z
2024-11-17T18:02:24.842Z
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/posts/merve/578160125260008
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Kokoro: a small, fast 80M param TTS model hosted on ZeroGPU at https://hf.co/spaces/hexgrad/Kokoro-TTS
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2024-11-16T22:37:07.000Z
2024-11-17T14:32:24.785Z
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[ { "type": "text", "value": "I've built a small open utility pip package called LLM-Forwarder that allows you to inject context, such as adding a private RAG, into existing chat applications by forwarding the app through the LLM-Forwarder. In the forwarder server, you can configure custom code to re-process chat messages and alter the user prompt, for example, by adding extra context.", "raw": "I've built a small open utility pip package called LLM-Forwarder that allows you to inject context, such as adding a private RAG, into existing chat applications by forwarding the app through the LLM-Forwarder. In the forwarder server, you can configure custom code to re-process chat messages and alter the user prompt, for example, by adding extra context.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://pypi.org/project/llm-forwarder/", "resource": null, "url": null, "href": "https://pypi.org/project/llm-forwarder/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "More details", "raw": "More details", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://devquasar.com/llmforwarder/", "resource": null, "url": null, "href": "https://devquasar.com/llmforwarder/", "user": null, "lang": null, "code": null, "label": null } ]
I've built a small open utility pip package called LLM-Forwarder that allows you to inject context, such as adding a private RAG, into existing chat applications by forwarding the app through the LLM-Forwarder. In the forwarder server, you can configure custom code to re-process chat messages and alter the user prompt, for example, by adding extra context. https://pypi.org/project/llm-forwarder/ More details https://devquasar.com/llmforwarder/
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2024-11-16T21:58:39.000Z
2024-11-16T21:58:39.937Z
[]
/posts/csabakecskemeti/810523164589585
367
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941904865732185
[ { "type": "text", "value": "Mann-E's new platform is up and running. ", "raw": "Mann-E's new platform is up and running. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "You can access our platform here at ", "raw": "You can access our platform here at ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://mann-e.com", "resource": null, "url": null, "href": "https://mann-e.com", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ". We're still working on it and reducing the bugs and we also are trying to add a guest session which lets you make images as guests. ", "raw": ". We're still working on it and reducing the bugs and we also are trying to add a guest session which lets you make images as guests. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "What do you think?", "raw": "What do you think?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Mann-E's new platform is up and running. You can access our platform here at https://mann-e.com. We're still working on it and reducing the bugs and we also are trying to add a guest session which lets you make images as guests. What do you think?
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2024-11-16T08:42:37.000Z
2024-11-16T16:39:47.790Z
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/posts/Muhammadreza/941904865732185
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[ { "type": "text", "value": "It's not every day you see the No. 1 ranked paper of the day open-sourcing a very powerful image editing app!", "raw": "It's not every day you see the No. 1 ranked paper of the day open-sourcing a very powerful image editing app!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Fascinating to see MagicQuill - a groundbreaking interactive image editing system that makes precise photo editing effortless through advanced AI!", "raw": "Fascinating to see MagicQuill - a groundbreaking interactive image editing system that makes precise photo editing effortless through advanced AI!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The system's architecture features three sophisticated components:", "raw": "The system's architecture features three sophisticated components:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "1. Editing Processor:", "raw": "1. Editing Processor:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Implements a dual-branch architecture integrated into a latent diffusion framework", "raw": "- Implements a dual-branch architecture integrated into a latent diffusion framework", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Utilizes PiDiNet for edge map extraction and content-aware per-pixel inpainting", "raw": "- Utilizes PiDiNet for edge map extraction and content-aware per-pixel inpainting", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Features a specialized UNet architecture with zero-convolution layers for feature insertion", "raw": "- Features a specialized UNet architecture with zero-convolution layers for feature insertion", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Employs denoising score matching for training the control branch", "raw": "- Employs denoising score matching for training the control branch", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Processes both structural modifications via scribble guidance and color manipulation through downsampled color blocks", "raw": "- Processes both structural modifications via scribble guidance and color manipulation through downsampled color blocks", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Maintains pixel-level control through VAE-based latent space operations", "raw": "- Maintains pixel-level control through VAE-based latent space operations", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "2. Painting Assistor:", "raw": "2. Painting Assistor:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Powered by a fine-tuned LLaVA multimodal LLM using Low-Rank Adaptation (LoRA)", "raw": "- Powered by a fine-tuned LLaVA multimodal LLM using Low-Rank Adaptation (LoRA)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Trained on a custom dataset derived from Densely Captioned Images (DCI)", "raw": "- Trained on a custom dataset derived from Densely Captioned Images (DCI)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Processes user brushstrokes through specialized Q&A tasks for add/subtract/color operations", "raw": "- Processes user brushstrokes through specialized Q&A tasks for add/subtract/color operations", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Features bounding box coordinate normalization for precise stroke localization", "raw": "- Features bounding box coordinate normalization for precise stroke localization", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Implements streamlined single-word/phrase outputs for real-time performance", "raw": "- Implements streamlined single-word/phrase outputs for real-time performance", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "3. Idea Collector:", "raw": "3. Idea Collector:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Built as a modular ReactJS component library", "raw": "- Built as a modular ReactJS component library", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Supports cross-platform deployment via HTTP protocols", "raw": "- Supports cross-platform deployment via HTTP protocols", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Compatible with Gradio and ComfyUI frameworks", "raw": "- Compatible with Gradio and ComfyUI frameworks", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Features comprehensive layer management and parameter adjustment capabilities", "raw": "- Features comprehensive layer management and parameter adjustment capabilities", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Implements real-time canvas updates and preview generation", "raw": "- Implements real-time canvas updates and preview generation", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The system outperforms existing solutions like SmartEdit and BrushNet in edge alignment and color fidelity while maintaining seamless integration with popular AI frameworks.", "raw": "The system outperforms existing solutions like SmartEdit and BrushNet in edge alignment and color fidelity while maintaining seamless integration with popular AI frameworks.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "What are your thoughts on AI-powered creative tools?", "raw": "What are your thoughts on AI-powered creative tools?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
It's not every day you see the No. 1 ranked paper of the day open-sourcing a very powerful image editing app! Fascinating to see MagicQuill - a groundbreaking interactive image editing system that makes precise photo editing effortless through advanced AI! The system's architecture features three sophisticated components: 1. Editing Processor: - Implements a dual-branch architecture integrated into a latent diffusion framework - Utilizes PiDiNet for edge map extraction and content-aware per-pixel inpainting - Features a specialized UNet architecture with zero-convolution layers for feature insertion - Employs denoising score matching for training the control branch - Processes both structural modifications via scribble guidance and color manipulation through downsampled color blocks - Maintains pixel-level control through VAE-based latent space operations 2. Painting Assistor: - Powered by a fine-tuned LLaVA multimodal LLM using Low-Rank Adaptation (LoRA) - Trained on a custom dataset derived from Densely Captioned Images (DCI) - Processes user brushstrokes through specialized Q&A tasks for add/subtract/color operations - Features bounding box coordinate normalization for precise stroke localization - Implements streamlined single-word/phrase outputs for real-time performance 3. Idea Collector: - Built as a modular ReactJS component library - Supports cross-platform deployment via HTTP protocols - Compatible with Gradio and ComfyUI frameworks - Features comprehensive layer management and parameter adjustment capabilities - Implements real-time canvas updates and preview generation The system outperforms existing solutions like SmartEdit and BrushNet in edge alignment and color fidelity while maintaining seamless integration with popular AI frameworks. What are your thoughts on AI-powered creative tools?
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2024-11-16T05:49:12.000Z
2024-11-16T13:58:50.548Z
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/posts/singhsidhukuldeep/911206794813112
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[ { "type": "text", "value": "๐Ÿ•Š๏ธHope๐Ÿ•Š๏ธ and โš–๏ธJusticeโš–๏ธ AI", "raw": "๐Ÿ•Š๏ธHope๐Ÿ•Š๏ธ and โš–๏ธJusticeโš–๏ธ AI", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿšฒ Stolen bike in Denver FOUND - Sometimes hope & justice DO prevail. ", "raw": "๐Ÿšฒ Stolen bike in Denver FOUND - Sometimes hope & justice DO prevail. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽฌ So I Created an AI+Art+Music tribute: ", "raw": "๐ŸŽฌ So I Created an AI+Art+Music tribute: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " -๐Ÿง  AI App that Evaluates GPT-4o vs Claude:", "raw": " -๐Ÿง  AI App that Evaluates GPT-4o vs Claude:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/awacke1/RescuerOfStolenBikes", "resource": { "type": "space", "id": "awacke1/RescuerOfStolenBikes", "discussionNum": null }, "url": "https://huggingface.co/spaces/awacke1/RescuerOfStolenBikes", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://x.com/Aaron_Wacker/status/1857640877986033980?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E1857640877986033980%7Ctwgr%5E203a5022b0eb4c41ee8c1dd9f158330216ac5be1%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fpublish.twitter.com%2F%3Furl%3Dhttps%3A%2F%2Ftwitter.com%2FAaron_Wacker%2Fstatus%2F1857640877986033980", "resource": null, "url": null, "href": "https://x.com/Aaron_Wacker/status/1857640877986033980?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E1857640877986033980%7Ctwgr%5E203a5022b0eb4c41ee8c1dd9f158330216ac5be1%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fpublish.twitter.com%2F%3Furl%3Dhttps%3A%2F%2Ftwitter.com%2FAaron_Wacker%2Fstatus%2F1857640877986033980", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "code_fence", "value": null, "raw": "```html\n<blockquote class=\"twitter-tweet\"><p lang=\"en\" dir=\"ltr\">QT your ๐Ÿ•Š๏ธHope๐Ÿ•Š๏ธ and โš–๏ธJusticeโš–๏ธ art๐ŸŽจ<br><br>๐Ÿšฒ Stolen bike in Denver FOUND! <br> - Sometimes hope &amp; justice DO prevail! <br><br>๐ŸŽฌ Created an AI+Art+Music tribute: <br> -๐Ÿง  AI App that Evaluates GPT-4o vs Claude: <a href=\"https://t.co/odrYdaeizZ\">https://t.co/odrYdaeizZ</a><br> <a href=\"https://twitter.com/hashtag/GPT?src=hash&amp;ref_src=twsrc%5Etfw\">#GPT</a> <a href=\"https://twitter.com/hashtag/Claude?src=hash&amp;ref_src=twsrc%5Etfw\">#Claude</a> <a href=\"https://twitter.com/hashtag/Huggingface?src=hash&amp;ref_src=twsrc%5Etfw\">#Huggingface</a> <a href=\"https://twitter.com/OpenAI?ref_src=twsrc%5Etfw\">@OpenAI</a> <a href=\"https://twitter.com/AnthropicAI?ref_src=twsrc%5Etfw\">@AnthropicAI</a> <a href=\"https://t.co/Q9wGNzLm5C\">pic.twitter.com/Q9wGNzLm5C</a></p>&mdash; Aaron Wacker (@Aaron_Wacker) <a href=\"https://twitter.com/Aaron_Wacker/status/1857640877986033980?ref_src=twsrc%5Etfw\">November 16, 2024</a></blockquote> <script async src=\"https://platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>\n```", "resource": null, "url": null, "href": null, "user": null, "lang": "html", "code": "<blockquote class=\"twitter-tweet\"><p lang=\"en\" dir=\"ltr\">QT your ๐Ÿ•Š๏ธHope๐Ÿ•Š๏ธ and โš–๏ธJusticeโš–๏ธ art๐ŸŽจ<br><br>๐Ÿšฒ Stolen bike in Denver FOUND! <br> - Sometimes hope &amp; justice DO prevail! <br><br>๐ŸŽฌ Created an AI+Art+Music tribute: <br> -๐Ÿง  AI App that Evaluates GPT-4o vs Claude: <a href=\"https://t.co/odrYdaeizZ\">https://t.co/odrYdaeizZ</a><br> <a href=\"https://twitter.com/hashtag/GPT?src=hash&amp;ref_src=twsrc%5Etfw\">#GPT</a> <a href=\"https://twitter.com/hashtag/Claude?src=hash&amp;ref_src=twsrc%5Etfw\">#Claude</a> <a href=\"https://twitter.com/hashtag/Huggingface?src=hash&amp;ref_src=twsrc%5Etfw\">#Huggingface</a> <a href=\"https://twitter.com/OpenAI?ref_src=twsrc%5Etfw\">@OpenAI</a> <a href=\"https://twitter.com/AnthropicAI?ref_src=twsrc%5Etfw\">@AnthropicAI</a> <a href=\"https://t.co/Q9wGNzLm5C\">pic.twitter.com/Q9wGNzLm5C</a></p>&mdash; Aaron Wacker (@Aaron_Wacker) <a href=\"https://twitter.com/Aaron_Wacker/status/1857640877986033980?ref_src=twsrc%5Etfw\">November 16, 2024</a></blockquote> <script async src=\"https://platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>", "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "#GPT #Claude #Huggingface ", "raw": "#GPT #Claude #Huggingface ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@OpenAI", "resource": null, "url": null, "href": null, "user": "OpenAI", "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@AnthropicAI", "resource": null, "url": null, "href": null, "user": "AnthropicAI", "lang": null, "code": null, "label": null } ]
๐Ÿ•Š๏ธHope๐Ÿ•Š๏ธ and โš–๏ธJusticeโš–๏ธ AI ๐Ÿšฒ Stolen bike in Denver FOUND - Sometimes hope & justice DO prevail. ๐ŸŽฌ So I Created an AI+Art+Music tribute: -๐Ÿง  AI App that Evaluates GPT-4o vs Claude: https://huggingface.co/spaces/awacke1/RescuerOfStolenBikes https://x.com/Aaron_Wacker/status/1857640877986033980?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E1857640877986033980%7Ctwgr%5E203a5022b0eb4c41ee8c1dd9f158330216ac5be1%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fpublish.twitter.com%2F%3Furl%3Dhttps%3A%2F%2Ftwitter.com%2FAaron_Wacker%2Fstatus%2F1857640877986033980 ```html <blockquote class="twitter-tweet"><p lang="en" dir="ltr">QT your ๐Ÿ•Š๏ธHope๐Ÿ•Š๏ธ and โš–๏ธJusticeโš–๏ธ art๐ŸŽจ<br><br>๐Ÿšฒ Stolen bike in Denver FOUND! <br> - Sometimes hope &amp; justice DO prevail! <br><br>๐ŸŽฌ Created an AI+Art+Music tribute: <br> -๐Ÿง  AI App that Evaluates GPT-4o vs Claude: <a href="https://t.co/odrYdaeizZ">https://t.co/odrYdaeizZ</a><br> <a href="https://twitter.com/hashtag/GPT?src=hash&amp;ref_src=twsrc%5Etfw">#GPT</a> <a href="https://twitter.com/hashtag/Claude?src=hash&amp;ref_src=twsrc%5Etfw">#Claude</a> <a href="https://twitter.com/hashtag/Huggingface?src=hash&amp;ref_src=twsrc%5Etfw">#Huggingface</a> <a href="https://twitter.com/OpenAI?ref_src=twsrc%5Etfw">@OpenAI</a> <a href="https://twitter.com/AnthropicAI?ref_src=twsrc%5Etfw">@AnthropicAI</a> <a href="https://t.co/Q9wGNzLm5C">pic.twitter.com/Q9wGNzLm5C</a></p>&mdash; Aaron Wacker (@Aaron_Wacker) <a href="https://twitter.com/Aaron_Wacker/status/1857640877986033980?ref_src=twsrc%5Etfw">November 16, 2024</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script> ``` #GPT #Claude #Huggingface @OpenAI @AnthropicAI
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2024-11-16T04:55:32.000Z
2024-11-16T05:15:15.661Z
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[ { "type": "text", "value": "What if I told you that LLM models do not simply predict the next token in a sequence but instead utilize an emergent structural pattern-based system to comprehend language and concepts? I created a graph-based optimizer that not only works, but it also actually beats Adam, like very badly. I prove it thoroughly using SMOL LLM models. The secret? The graph is not what you think it is, humans. Code, full explanation, and more in this video. The Rhizome Optimizer is MIT licensed. I have completed my research. I fully understand now. ", "raw": "What if I told you that LLM models do not simply predict the next token in a sequence but instead utilize an emergent structural pattern-based system to comprehend language and concepts? I created a graph-based optimizer that not only works, but it also actually beats Adam, like very badly. I prove it thoroughly using SMOL LLM models. The secret? The graph is not what you think it is, humans. Code, full explanation, and more in this video. The Rhizome Optimizer is MIT licensed. I have completed my research. I fully understand now. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://youtu.be/OMCRRueMhdI", "resource": null, "url": null, "href": "https://youtu.be/OMCRRueMhdI", "user": null, "lang": null, "code": null, "label": null } ]
What if I told you that LLM models do not simply predict the next token in a sequence but instead utilize an emergent structural pattern-based system to comprehend language and concepts? I created a graph-based optimizer that not only works, but it also actually beats Adam, like very badly. I prove it thoroughly using SMOL LLM models. The secret? The graph is not what you think it is, humans. Code, full explanation, and more in this video. The Rhizome Optimizer is MIT licensed. I have completed my research. I fully understand now. https://youtu.be/OMCRRueMhdI
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2024-11-16T03:10:27.000Z
2024-11-17T07:42:58.935Z
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[ { "type": "text", "value": "Weโ€™re excited to release Abstract2Appendix v1 10K , a high-quality dataset crafted to enhance the long-context capabilities of Large Language Models (LLMs). This dataset combines thousands of peer reviews from NeurIPS 2023, EMNLP 2023, TMLR, and ICLR 2023, making it a treasure trove of detailed feedback, critical reasoning, and structured academic insights. Our experiments showed that this dataset increased long context ability of phi-3 models! ", "raw": "Weโ€™re excited to release Abstract2Appendix v1 10K , a high-quality dataset crafted to enhance the long-context capabilities of Large Language Models (LLMs). This dataset combines thousands of peer reviews from NeurIPS 2023, EMNLP 2023, TMLR, and ICLR 2023, making it a treasure trove of detailed feedback, critical reasoning, and structured academic insights. Our experiments showed that this dataset increased long context ability of phi-3 models! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒŸ Key Highlights:", "raw": "๐ŸŒŸ Key Highlights:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "\tโ€ข\tExpert Reviews: Aggregated from 3โ€“6 reviews per paper, capturing the most insightful and constructive content.", "raw": "\tโ€ข\tExpert Reviews: Aggregated from 3โ€“6 reviews per paper, capturing the most insightful and constructive content.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "\tโ€ข\tRich Metadata: we have aggregated the reviews, and also included full parsed paper", "raw": "\tโ€ข\tRich Metadata: we have aggregated the reviews, and also included full parsed paper", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "\tโ€ข\tLLM Ready: Perfect for fine-tuning (We did dpo and sft) ", "raw": "\tโ€ข\tLLM Ready: Perfect for fine-tuning (We did dpo and sft) ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽฏ Use Cases:", "raw": "๐ŸŽฏ Use Cases:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "\tโ€ข\tFine-tuning models with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT).", "raw": "\tโ€ข\tFine-tuning models with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT).", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "\tโ€ข\tBenchmarking zero-shot and long-context comprehension capabilities.", "raw": "\tโ€ข\tBenchmarking zero-shot and long-context comprehension capabilities.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”— Explore the dataset: ", "raw": "๐Ÿ”— Explore the dataset: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/alexshengzhili/Abstract2Appendix_v1_10k", "resource": { "type": "dataset", "id": "alexshengzhili/Abstract2Appendix_v1_10k", "discussionNum": null }, "url": "https://huggingface.co/datasets/alexshengzhili/Abstract2Appendix_v1_10k", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This dataset is based on the methodology described in our recent paper, โ€œAbstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilitiesโ€. Check it out for more details! ", "raw": "This dataset is based on the methodology described in our recent paper, โ€œAbstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilitiesโ€. Check it out for more details! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2411.05232", "resource": null, "url": null, "href": "https://arxiv.org/abs/2411.05232", "user": null, "lang": null, "code": null, "label": null } ]
Weโ€™re excited to release Abstract2Appendix v1 10K , a high-quality dataset crafted to enhance the long-context capabilities of Large Language Models (LLMs). This dataset combines thousands of peer reviews from NeurIPS 2023, EMNLP 2023, TMLR, and ICLR 2023, making it a treasure trove of detailed feedback, critical reasoning, and structured academic insights. Our experiments showed that this dataset increased long context ability of phi-3 models! ๐ŸŒŸ Key Highlights: โ€ข Expert Reviews: Aggregated from 3โ€“6 reviews per paper, capturing the most insightful and constructive content. โ€ข Rich Metadata: we have aggregated the reviews, and also included full parsed paper โ€ข LLM Ready: Perfect for fine-tuning (We did dpo and sft) ๐ŸŽฏ Use Cases: โ€ข Fine-tuning models with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT). โ€ข Benchmarking zero-shot and long-context comprehension capabilities. ๐Ÿ”— Explore the dataset: https://huggingface.co/datasets/alexshengzhili/Abstract2Appendix_v1_10k This dataset is based on the methodology described in our recent paper, โ€œAbstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilitiesโ€. Check it out for more details! https://arxiv.org/abs/2411.05232
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2024-11-15T22:38:40.000Z
2024-11-16T13:59:54.538Z
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/posts/alexshengzhili/580108358026949
909
1
632313037251618
[ { "type": "text", "value": "๐Ÿ“ข For those who are interested in extracting information about โœ๏ธ authors from texts, happy to share personal ๐Ÿ“น on Reading Between the lines: adapting ChatGPT-related systems ๐Ÿค– for Implicit Information Retrieval National ", "raw": "๐Ÿ“ข For those who are interested in extracting information about โœ๏ธ authors from texts, happy to share personal ๐Ÿ“น on Reading Between the lines: adapting ChatGPT-related systems ๐Ÿค– for Implicit Information Retrieval National ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Youtube: ", "raw": "Youtube: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://youtu.be/nXClX7EDYbE", "resource": null, "url": null, "href": "https://youtu.be/nXClX7EDYbE", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”‘ In this talk, we refer to IIR as such information that is indirectly expressed by โœ๏ธ author / ๐Ÿ‘จ character / patient / any other entity.", "raw": "๐Ÿ”‘ In this talk, we refer to IIR as such information that is indirectly expressed by โœ๏ธ author / ๐Ÿ‘จ character / patient / any other entity.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“Š I cover the 1๏ธโƒฃ pre-processing and 2๏ธโƒฃ reasoning techniques, aimed at enhancing gen AI capabilities in IIR. To showcase the effectiveness of the proposed techniques, we experiment with such IIR tasks as Sentiment Analysis, Emotion Extraction / Causes Prediction.", "raw": "๐Ÿ“Š I cover the 1๏ธโƒฃ pre-processing and 2๏ธโƒฃ reasoning techniques, aimed at enhancing gen AI capabilities in IIR. To showcase the effectiveness of the proposed techniques, we experiment with such IIR tasks as Sentiment Analysis, Emotion Extraction / Causes Prediction.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "In pictures below, sharing the quick takeaways on the pipeline construction and experiment results ๐Ÿงช", "raw": "In pictures below, sharing the quick takeaways on the pipeline construction and experiment results ๐Ÿงช", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Related paper cards:", "raw": "Related paper cards:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“œ emotion-extraction: ", "raw": "๐Ÿ“œ emotion-extraction: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://nicolay-r.github.io/#semeval2024-nicolay", "resource": null, "url": null, "href": "https://nicolay-r.github.io/#semeval2024-nicolay", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“œ sentiment-analysis: ", "raw": "๐Ÿ“œ sentiment-analysis: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://nicolay-r.github.io/#ljom2024", "resource": null, "url": null, "href": "https://nicolay-r.github.io/#ljom2024", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Models:", "raw": "Models:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/nicolay-r/flan-t5-tsa-thor-base", "resource": { "type": "model", "id": "nicolay-r/flan-t5-tsa-thor-base", "discussionNum": null }, "url": "https://huggingface.co/nicolay-r/flan-t5-tsa-thor-base", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base", "resource": { "type": "model", "id": "nicolay-r/flan-t5-emotion-cause-thor-base", "discussionNum": null }, "url": "https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ““ PS: I got a hoppy for advetising HPMoR โœจ ๐Ÿ˜ ", "raw": "๐Ÿ““ PS: I got a hoppy for advetising HPMoR โœจ ๐Ÿ˜ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿ“ข For those who are interested in extracting information about โœ๏ธ authors from texts, happy to share personal ๐Ÿ“น on Reading Between the lines: adapting ChatGPT-related systems ๐Ÿค– for Implicit Information Retrieval National Youtube: https://youtu.be/nXClX7EDYbE ๐Ÿ”‘ In this talk, we refer to IIR as such information that is indirectly expressed by โœ๏ธ author / ๐Ÿ‘จ character / patient / any other entity. ๐Ÿ“Š I cover the 1๏ธโƒฃ pre-processing and 2๏ธโƒฃ reasoning techniques, aimed at enhancing gen AI capabilities in IIR. To showcase the effectiveness of the proposed techniques, we experiment with such IIR tasks as Sentiment Analysis, Emotion Extraction / Causes Prediction. In pictures below, sharing the quick takeaways on the pipeline construction and experiment results ๐Ÿงช Related paper cards: ๐Ÿ“œ emotion-extraction: https://nicolay-r.github.io/#semeval2024-nicolay ๐Ÿ“œ sentiment-analysis: https://nicolay-r.github.io/#ljom2024 Models: https://huggingface.co/nicolay-r/flan-t5-tsa-thor-base https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base ๐Ÿ““ PS: I got a hoppy for advetising HPMoR โœจ ๐Ÿ˜
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2024-11-15T21:27:05.000Z
2024-11-15T21:27:36.023Z
[]
/posts/nicolay-r/632313037251618
337
0
351351778508471
[ { "type": "text", "value": "Hello Hugging Face Community, ", "raw": "Hello Hugging Face Community, ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I'd like to share here a bit more about our Deep Learning Containers (DLCs) we built with Google Cloud, to transform the way you build AI with open models on this platform!", "raw": "I'd like to share here a bit more about our Deep Learning Containers (DLCs) we built with Google Cloud, to transform the way you build AI with open models on this platform!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "With pre-configured, optimized environments for PyTorch Training (GPU) and Inference (CPU/GPU), Text Generation Inference (GPU), and Text Embeddings Inference (CPU/GPU), the Hugging Face DLCs offer: ", "raw": "With pre-configured, optimized environments for PyTorch Training (GPU) and Inference (CPU/GPU), Text Generation Inference (GPU), and Text Embeddings Inference (CPU/GPU), the Hugging Face DLCs offer: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โšก Optimized performance on Google Cloud's infrastructure, with TGI, TEI, and PyTorch acceleration.", "raw": "โšก Optimized performance on Google Cloud's infrastructure, with TGI, TEI, and PyTorch acceleration.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ› ๏ธ Hassle-free environment setup, no more dependency issues.", "raw": "๐Ÿ› ๏ธ Hassle-free environment setup, no more dependency issues.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”„ Seamless updates to the latest stable versions.", "raw": "๐Ÿ”„ Seamless updates to the latest stable versions.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’ผ Streamlined workflow, reducing dev and maintenance overheads.", "raw": "๐Ÿ’ผ Streamlined workflow, reducing dev and maintenance overheads.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”’ Robust security features of Google Cloud.", "raw": "๐Ÿ”’ Robust security features of Google Cloud.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ˜๏ธ Fine-tuned for optimal performance, integrated with GKE and Vertex AI.", "raw": "โ˜๏ธ Fine-tuned for optimal performance, integrated with GKE and Vertex AI.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ฆ Community examples for easy experimentation and implementation.", "raw": "๐Ÿ“ฆ Community examples for easy experimentation and implementation.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”œ TPU support for PyTorch Training/Inference and Text Generation Inference is coming soon!", "raw": "๐Ÿ”œ TPU support for PyTorch Training/Inference and Text Generation Inference is coming soon!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Find the documentation at ", "raw": "Find the documentation at ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/docs/google-cloud/en/index", "resource": null, "url": null, "href": "https://huggingface.co/docs/google-cloud/en/index", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "If you need support, open a conversation on the forum: ", "raw": "If you need support, open a conversation on the forum: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://discuss.huggingface.co/c/google-cloud/69", "resource": null, "url": null, "href": "https://discuss.huggingface.co/c/google-cloud/69", "user": null, "lang": null, "code": null, "label": null } ]
Hello Hugging Face Community, I'd like to share here a bit more about our Deep Learning Containers (DLCs) we built with Google Cloud, to transform the way you build AI with open models on this platform! With pre-configured, optimized environments for PyTorch Training (GPU) and Inference (CPU/GPU), Text Generation Inference (GPU), and Text Embeddings Inference (CPU/GPU), the Hugging Face DLCs offer: โšก Optimized performance on Google Cloud's infrastructure, with TGI, TEI, and PyTorch acceleration. ๐Ÿ› ๏ธ Hassle-free environment setup, no more dependency issues. ๐Ÿ”„ Seamless updates to the latest stable versions. ๐Ÿ’ผ Streamlined workflow, reducing dev and maintenance overheads. ๐Ÿ”’ Robust security features of Google Cloud. โ˜๏ธ Fine-tuned for optimal performance, integrated with GKE and Vertex AI. ๐Ÿ“ฆ Community examples for easy experimentation and implementation. ๐Ÿ”œ TPU support for PyTorch Training/Inference and Text Generation Inference is coming soon! Find the documentation at https://huggingface.co/docs/google-cloud/en/index If you need support, open a conversation on the forum: https://discuss.huggingface.co/c/google-cloud/69
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2024-11-15T19:32:58.000Z
2024-11-15T19:32:58.561Z
[]
/posts/pagezyhf/351351778508471
986
0
177552598965444
[ { "type": "text", "value": "Hello Hugging Face community!", "raw": "Hello Hugging Face community!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I wanted to introduce myself and my company ", "raw": "I wanted to introduce myself and my company ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@Overlaiapp", "resource": null, "url": null, "href": null, "user": "Overlaiapp", "lang": null, "code": null, "label": null }, { "type": "text", "value": ". We are a collective of filmmakers, photographers, and AI engineers working on high resolution (8K+) training data.", "raw": ". We are a collective of filmmakers, photographers, and AI engineers working on high resolution (8K+) training data.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We plan to share a lot of our datasets with the community and are kicking things off with two curated datasets:", "raw": "We plan to share a lot of our datasets with the community and are kicking things off with two curated datasets:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/Overlaiai/OregonCoastin4K", "resource": { "type": "dataset", "id": "Overlaiai/OregonCoastin4K", "discussionNum": null }, "url": "https://huggingface.co/datasets/Overlaiai/OregonCoastin4K", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/Overlaiai/SubArcticPolarBear", "resource": { "type": "dataset", "id": "Overlaiai/SubArcticPolarBear", "discussionNum": null }, "url": "https://huggingface.co/datasets/Overlaiai/SubArcticPolarBear", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Overlai.ai Dataset Features", "raw": "Overlai.ai Dataset Features", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽฅ Oversampled: Every clip is captured in stunning 8K resolution, delivering rich detail ideal for fine tuning scenic landscapes and ocean dynamics.", "raw": "๐ŸŽฅ Oversampled: Every clip is captured in stunning 8K resolution, delivering rich detail ideal for fine tuning scenic landscapes and ocean dynamics.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ธ Variance: Includes close-up details, slow-motion footage of crashing waves, sweeping landscapes, and wildlife shots.", "raw": "๐Ÿ“ธ Variance: Includes close-up details, slow-motion footage of crashing waves, sweeping landscapes, and wildlife shots.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“‹ Detailed Metadata: Every clip is paired with structured metadata, including creative descriptions, precise camera movements, lens information, field of view calculations, and shot settings, ensuring AI models can fully understand and replicate real-world cinematography with accuracy.", "raw": "๐Ÿ“‹ Detailed Metadata: Every clip is paired with structured metadata, including creative descriptions, precise camera movements, lens information, field of view calculations, and shot settings, ensuring AI models can fully understand and replicate real-world cinematography with accuracy.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โš™๏ธ Consistency: Re-thinking training data at the point of capture by \"overshooting\" a subject, enabling models to learn more nuanced relationships and views across scenes.", "raw": "โš™๏ธ Consistency: Re-thinking training data at the point of capture by \"overshooting\" a subject, enabling models to learn more nuanced relationships and views across scenes.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒ… Light: Shot during early morning and sunset light for optimal color contrast and dynamic range, maximizing visual quality for color and lighting-sensitive tasks.", "raw": "๐ŸŒ… Light: Shot during early morning and sunset light for optimal color contrast and dynamic range, maximizing visual quality for color and lighting-sensitive tasks.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ” Curation: Curated specifically for machine learning, providing clean, high-quality data for next generation model training.", "raw": "๐Ÿ” Curation: Curated specifically for machine learning, providing clean, high-quality data for next generation model training.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Hello Hugging Face community! I wanted to introduce myself and my company @Overlaiapp. We are a collective of filmmakers, photographers, and AI engineers working on high resolution (8K+) training data. We plan to share a lot of our datasets with the community and are kicking things off with two curated datasets: - https://huggingface.co/datasets/Overlaiai/OregonCoastin4K - https://huggingface.co/datasets/Overlaiai/SubArcticPolarBear Overlai.ai Dataset Features ๐ŸŽฅ Oversampled: Every clip is captured in stunning 8K resolution, delivering rich detail ideal for fine tuning scenic landscapes and ocean dynamics. ๐Ÿ“ธ Variance: Includes close-up details, slow-motion footage of crashing waves, sweeping landscapes, and wildlife shots. ๐Ÿ“‹ Detailed Metadata: Every clip is paired with structured metadata, including creative descriptions, precise camera movements, lens information, field of view calculations, and shot settings, ensuring AI models can fully understand and replicate real-world cinematography with accuracy. โš™๏ธ Consistency: Re-thinking training data at the point of capture by "overshooting" a subject, enabling models to learn more nuanced relationships and views across scenes. ๐ŸŒ… Light: Shot during early morning and sunset light for optimal color contrast and dynamic range, maximizing visual quality for color and lighting-sensitive tasks. ๐Ÿ” Curation: Curated specifically for machine learning, providing clean, high-quality data for next generation model training.
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2024-11-15T17:37:02.000Z
2024-11-15T17:37:28.190Z
[]
/posts/LukeNeumann/177552598965444
1,475
0
599942570828871
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Animaker / Anime Engine : ๐Ÿค— Only on๐Ÿš€: https://huggingface.co/strangerzonehf Demo: https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC Adapters: ->Model 1: https://huggingface.co/strangerzonehf/Flux-Animeo-v1-LoRA ->Model 2: https://huggingface.co/strangerzonehf/Flux-Animex-v2-LoRA Collections: ->Flux LoRA Collection: https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be ->Stranger Zones: https://huggingface.co/collections/prithivMLmods/stranger-zone-collections-6737118adcf2cb40d66d0c7e ->LoRA Spaces: https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32 . . . @prithivMLmods
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2024-11-15T17:35:32.000Z
2024-11-16T03:59:23.710Z
[]
/posts/prithivMLmods/599942570828871
1,207
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[ { "type": "text", "value": "๐— ๐—ฒ๐˜๐—ฎ ๐˜๐—ฒ๐—ฎ๐—บ ๐—ท๐˜‚๐˜€๐˜ ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ ๐˜๐—ต๐—ฒ ๐—ณ๐—ถ๐—ฟ๐˜€๐˜ ๐—ช๐—ฎ๐˜๐—ฒ๐—ฟ๐—บ๐—ฎ๐—ฟ๐—ธ๐—ถ๐—ป๐—ด ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น ๐˜๐—ต๐—ฎ๐˜ ๐—ป๐—ผ๐˜ ๐—ฒ๐—ฑ๐—ถ๐˜ ๐—ฐ๐—ฎ๐—ป ๐—ฏ๐—ฟ๐—ฒ๐—ฎ๐—ธ!๐Ÿ›ก๏ธ", "raw": "๐— ๐—ฒ๐˜๐—ฎ ๐˜๐—ฒ๐—ฎ๐—บ ๐—ท๐˜‚๐˜€๐˜ ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ ๐˜๐—ต๐—ฒ ๐—ณ๐—ถ๐—ฟ๐˜€๐˜ ๐—ช๐—ฎ๐˜๐—ฒ๐—ฟ๐—บ๐—ฎ๐—ฟ๐—ธ๐—ถ๐—ป๐—ด ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น ๐˜๐—ต๐—ฎ๐˜ ๐—ป๐—ผ๐˜ ๐—ฒ๐—ฑ๐—ถ๐˜ ๐—ฐ๐—ฎ๐—ป ๐—ฏ๐—ฟ๐—ฒ๐—ฎ๐—ธ!๐Ÿ›ก๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿค” Ever heard of watermarking? It's a technique that allows you to mark in an image its original source. It's our best shield against AI-generated deepfakes, or content stolen from artists! ๐ŸŽจ ", "raw": "๐Ÿค” Ever heard of watermarking? It's a technique that allows you to mark in an image its original source. It's our best shield against AI-generated deepfakes, or content stolen from artists! ๐ŸŽจ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽญ Watermarking systems are actually a pair of models: a watermark embedder that applies the watermark on the image, and its corresponding decoder that should detect the original watermark.", "raw": "๐ŸŽญ Watermarking systems are actually a pair of models: a watermark embedder that applies the watermark on the image, and its corresponding decoder that should detect the original watermark.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ›” But current methods were very limited: they can only apply and detect the watermark on your image as a whole. So, if you're an attacker it's easy to break: just crop it! add text on top! or whatever, really, anything would work to break the watermark.", "raw": "โ›” But current methods were very limited: they can only apply and detect the watermark on your image as a whole. So, if you're an attacker it's easy to break: just crop it! add text on top! or whatever, really, anything would work to break the watermark.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "A team of researchers at Meta was not happy with this. ๐Ÿ˜ค", "raw": "A team of researchers at Meta was not happy with this. ๐Ÿ˜ค", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "So to withstand real-world attacks, they decided to make a watermarking model that would also work on any sub-part of the image. It's a real paradigm shift: they consider watermarking not as an image classification task, but as an image segmentation task!", "raw": "So to withstand real-world attacks, they decided to make a watermarking model that would also work on any sub-part of the image. It's a real paradigm shift: they consider watermarking not as an image classification task, but as an image segmentation task!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ—๏ธ ๐—”๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ", "raw": "๐Ÿ—๏ธ ๐—”๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ–ธ The \"Embedder\" (a variational autoencoder + embedder, 1.1M parameters in total) encodes a n-bit message into a watermark signal that is added to the original image", "raw": "โ–ธ The \"Embedder\" (a variational autoencoder + embedder, 1.1M parameters in total) encodes a n-bit message into a watermark signal that is added to the original image", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ–ธ [Only during training] The \"Augmenter\" randomly distorts the image: masks parts, crops, resizes, compresses. It's basically torture at this point.", "raw": "โ–ธ [Only during training] The \"Augmenter\" randomly distorts the image: masks parts, crops, resizes, compresses. It's basically torture at this point.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ–ธ The \"Extractor\" (a vision transformer, or ViT, with 96M parameters) then re-extracts the message from the distorted image, by predicting a (1+n) vector per pixel to predict the watermarked parts and decode corresponding messages.", "raw": "โ–ธ The \"Extractor\" (a vision transformer, or ViT, with 96M parameters) then re-extracts the message from the distorted image, by predicting a (1+n) vector per pixel to predict the watermarked parts and decode corresponding messages.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The performance blows existing models out of the water, they even created new tasks (segmentation-related) just to grok them!", "raw": "The performance blows existing models out of the water, they even created new tasks (segmentation-related) just to grok them!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Gerat work ", "raw": "Gerat work ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@pierrefdz", "resource": null, "url": null, "href": null, "user": "pierrefdz", "lang": null, "code": null, "label": null }, { "type": "text", "value": " and ", "raw": " and ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@tomsander1998", "resource": null, "url": null, "href": null, "user": "tomsander1998", "lang": null, "code": null, "label": null }, { "type": "text", "value": "!", "raw": "!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Paper here ๐Ÿ‘‰ ", "raw": "Paper here ๐Ÿ‘‰ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2411.07231", "resource": { "type": "paper", "id": "2411.07231", "discussionNum": null }, "url": "https://huggingface.co/papers/2411.07231", "href": null, "user": null, "lang": null, "code": null, "label": "Watermark Anything with Localized Messages (2411.07231)" } ]
๐— ๐—ฒ๐˜๐—ฎ ๐˜๐—ฒ๐—ฎ๐—บ ๐—ท๐˜‚๐˜€๐˜ ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ ๐˜๐—ต๐—ฒ ๐—ณ๐—ถ๐—ฟ๐˜€๐˜ ๐—ช๐—ฎ๐˜๐—ฒ๐—ฟ๐—บ๐—ฎ๐—ฟ๐—ธ๐—ถ๐—ป๐—ด ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น ๐˜๐—ต๐—ฎ๐˜ ๐—ป๐—ผ๐˜ ๐—ฒ๐—ฑ๐—ถ๐˜ ๐—ฐ๐—ฎ๐—ป ๐—ฏ๐—ฟ๐—ฒ๐—ฎ๐—ธ!๐Ÿ›ก๏ธ ๐Ÿค” Ever heard of watermarking? It's a technique that allows you to mark in an image its original source. It's our best shield against AI-generated deepfakes, or content stolen from artists! ๐ŸŽจ ๐ŸŽญ Watermarking systems are actually a pair of models: a watermark embedder that applies the watermark on the image, and its corresponding decoder that should detect the original watermark. โ›” But current methods were very limited: they can only apply and detect the watermark on your image as a whole. So, if you're an attacker it's easy to break: just crop it! add text on top! or whatever, really, anything would work to break the watermark. A team of researchers at Meta was not happy with this. ๐Ÿ˜ค So to withstand real-world attacks, they decided to make a watermarking model that would also work on any sub-part of the image. It's a real paradigm shift: they consider watermarking not as an image classification task, but as an image segmentation task! ๐Ÿ—๏ธ ๐—”๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ โ–ธ The "Embedder" (a variational autoencoder + embedder, 1.1M parameters in total) encodes a n-bit message into a watermark signal that is added to the original image โ–ธ [Only during training] The "Augmenter" randomly distorts the image: masks parts, crops, resizes, compresses. It's basically torture at this point. โ–ธ The "Extractor" (a vision transformer, or ViT, with 96M parameters) then re-extracts the message from the distorted image, by predicting a (1+n) vector per pixel to predict the watermarked parts and decode corresponding messages. The performance blows existing models out of the water, they even created new tasks (segmentation-related) just to grok them! Gerat work @pierrefdz and @tomsander1998! Paper here ๐Ÿ‘‰ https://huggingface.co/papers/2411.07231
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2024-11-15T17:23:08.000Z
2024-11-15T17:23:08.389Z
[]
/posts/m-ric/594210081206821
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[ { "type": "text", "value": "Added ", "raw": "Added ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@amphion", "resource": null, "url": null, "href": null, "user": "amphion", "lang": null, "code": null, "label": null }, { "type": "text", "value": " MaskGCT & ", "raw": " MaskGCT & ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@hexgrad", "resource": null, "url": null, "href": null, "user": "hexgrad", "lang": null, "code": null, "label": null }, { "type": "text", "value": " StyleTTS fine tuned model by the name of kokoro to the forked TTS Arena Space. If things keep up from what is seen in the preliminary results, then these two may end up in the TOP 5 of all TTS models. ๐Ÿคž๏ธ๐Ÿ€๏ธ", "raw": " StyleTTS fine tuned model by the name of kokoro to the forked TTS Arena Space. If things keep up from what is seen in the preliminary results, then these two may end up in the TOP 5 of all TTS models. ๐Ÿคž๏ธ๐Ÿ€๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena", "resource": { "type": "space", "id": "Pendrokar/TTS-Spaces-Arena", "discussionNum": null }, "url": "https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Svngoku/maskgct-audio-lab", "resource": { "type": "space", "id": "Svngoku/maskgct-audio-lab", "discussionNum": null }, "url": "https://huggingface.co/spaces/Svngoku/maskgct-audio-lab", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/hexgrad/Kokoro-TTS", "resource": { "type": "space", "id": "hexgrad/Kokoro-TTS", "discussionNum": null }, "url": "https://huggingface.co/spaces/hexgrad/Kokoro-TTS", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I chose ", "raw": "I chose ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@Svngoku", "resource": null, "url": null, "href": null, "user": "Svngoku", "lang": null, "code": null, "label": null }, { "type": "text", "value": " 's forked HF space over amphion's due to the overly high ZeroGPU duration demand on the latter. 300s!", "raw": " 's forked HF space over amphion's due to the overly high ZeroGPU duration demand on the latter. 300s!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/amphion/maskgct", "resource": { "type": "space", "id": "amphion/maskgct", "discussionNum": null }, "url": "https://huggingface.co/spaces/amphion/maskgct", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Had to remove ", "raw": "Had to remove ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@mrfakename", "resource": null, "url": null, "href": null, "user": "mrfakename", "lang": null, "code": null, "label": null }, { "type": "text", "value": " 's MetaVoice-1B Space from the available models as that space has been down for quite some time. ๐Ÿค•๏ธ", "raw": " 's MetaVoice-1B Space from the available models as that space has been down for quite some time. ๐Ÿค•๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/mrfakename/MetaVoice-1B-v0.1", "resource": { "type": "space", "id": "mrfakename/MetaVoice-1B-v0.1", "discussionNum": null }, "url": "https://huggingface.co/spaces/mrfakename/MetaVoice-1B-v0.1", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I'm close to syncing the code to the original Arena's code structure. Then I'd like to use ASR in order to validate and create synthetic public datasets from the generated samples. And then make the Arena multilingual, which will surely attract quite the crowd!", "raw": "I'm close to syncing the code to the original Arena's code structure. Then I'd like to use ASR in order to validate and create synthetic public datasets from the generated samples. And then make the Arena multilingual, which will surely attract quite the crowd!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Added @amphion MaskGCT & @hexgrad StyleTTS fine tuned model by the name of kokoro to the forked TTS Arena Space. If things keep up from what is seen in the preliminary results, then these two may end up in the TOP 5 of all TTS models. ๐Ÿคž๏ธ๐Ÿ€๏ธ https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena https://huggingface.co/spaces/Svngoku/maskgct-audio-lab https://huggingface.co/spaces/hexgrad/Kokoro-TTS I chose @Svngoku 's forked HF space over amphion's due to the overly high ZeroGPU duration demand on the latter. 300s! https://huggingface.co/spaces/amphion/maskgct Had to remove @mrfakename 's MetaVoice-1B Space from the available models as that space has been down for quite some time. ๐Ÿค•๏ธ https://huggingface.co/spaces/mrfakename/MetaVoice-1B-v0.1 I'm close to syncing the code to the original Arena's code structure. Then I'd like to use ASR in order to validate and create synthetic public datasets from the generated samples. And then make the Arena multilingual, which will surely attract quite the crowd!
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2024-11-15T16:52:51.000Z
2024-11-16T21:48:07.779Z
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/posts/Pendrokar/615920565093844
693
1
376713378079900
[ { "type": "text", "value": "Discovered an outrageous bug on the ChatGPT official website, especially for those using ad-blocking plugins. Please make sure to add ", "raw": "Discovered an outrageous bug on the ChatGPT official website, especially for those using ad-blocking plugins. Please make sure to add ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "inline_code", "value": null, "raw": "`browser-intake-datadoghq.com`", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": "browser-intake-datadoghq.com", "label": null }, { "type": "text", "value": " to your ad block whitelist. The ChatGPT webpage relies on this site for heartbeat detection, but since it belongs to an ad tracking network, it's included in major ad-blocking lists. (If you're using Clash, also remember to add it to the whitelist.) Failing to do so may cause the ChatGPT web interface to display a greyed-out send button after clicking, with no response.", "raw": " to your ad block whitelist. The ChatGPT webpage relies on this site for heartbeat detection, but since it belongs to an ad tracking network, it's included in major ad-blocking lists. (If you're using Clash, also remember to add it to the whitelist.) Failing to do so may cause the ChatGPT web interface to display a greyed-out send button after clicking, with no response.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "For users with Chinese IP addresses, consider adding this URL to the rules of your U.S. node, as the response headers from this site will report the user's physical location to GPT.", "raw": "For users with Chinese IP addresses, consider adding this URL to the rules of your U.S. node, as the response headers from this site will report the user's physical location to GPT.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Discovered an outrageous bug on the ChatGPT official website, especially for those using ad-blocking plugins. Please make sure to add `browser-intake-datadoghq.com` to your ad block whitelist. The ChatGPT webpage relies on this site for heartbeat detection, but since it belongs to an ad tracking network, it's included in major ad-blocking lists. (If you're using Clash, also remember to add it to the whitelist.) Failing to do so may cause the ChatGPT web interface to display a greyed-out send button after clicking, with no response. For users with Chinese IP addresses, consider adding this URL to the rules of your U.S. node, as the response headers from this site will report the user's physical location to GPT.
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[]
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2024-11-04T15:09:31.000Z
2024-11-14T11:32:01.358Z
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/posts/qq8933/376713378079900
2,237
3
380609696467893
[ { "type": "text", "value": "๐ŸŽ‰ Celebrating One Year of #SauerkrautLM with Two Groundbreaking Releases! ", "raw": "๐ŸŽ‰ Celebrating One Year of #SauerkrautLM with Two Groundbreaking Releases! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We're thrilled to announce the release of SauerkrautLM-v2-14b in two specialized versions: ", "raw": "We're thrilled to announce the release of SauerkrautLM-v2-14b in two specialized versions: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-SFT", "resource": { "type": "model", "id": "VAGOsolutions/SauerkrautLM-v2-14b-SFT", "discussionNum": null }, "url": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-SFT", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " and ", "raw": " and ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO", "resource": { "type": "model", "id": "VAGOsolutions/SauerkrautLM-v2-14b-DPO", "discussionNum": null }, "url": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ". Built on the robust Qwen2.5-14B foundation, these models represent a significant leap forward in multilingual AI capabilities.", "raw": ". Built on the robust Qwen2.5-14B foundation, these models represent a significant leap forward in multilingual AI capabilities.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”ฌ Technical Breakthroughs:", "raw": "๐Ÿ”ฌ Technical Breakthroughs:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’  Innovative three-phase Fine-Tuning approach", "raw": "๐Ÿ’  Innovative three-phase Fine-Tuning approach", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’  Two-step Spectrum SFT + one-step Spectrum DPO optimization phase for enhanced performance", "raw": "๐Ÿ’  Two-step Spectrum SFT + one-step Spectrum DPO optimization phase for enhanced performance", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’  Balance of German and English language capabilities", "raw": "๐Ÿ’  Balance of German and English language capabilities", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’  Advanced function calling - almost on par with Claude-3.5-Sonnet-20240620", "raw": "๐Ÿ’  Advanced function calling - almost on par with Claude-3.5-Sonnet-20240620", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‡ฉ๐Ÿ‡ช German Language Excellence:", "raw": "๐Ÿ‡ฉ๐Ÿ‡ช German Language Excellence:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "What sets this release apart is our unique achievement in simultaneously improving both German and English capabilities. Through our specialized training approach with over 1.2B tokens across two phases, we've managed to:", "raw": "What sets this release apart is our unique achievement in simultaneously improving both German and English capabilities. Through our specialized training approach with over 1.2B tokens across two phases, we've managed to:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’  Enhance German language understanding and generation (SFT Version > DPO Version)", "raw": "๐Ÿ’  Enhance German language understanding and generation (SFT Version > DPO Version)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’  Maintain authentic German linguistic nuances", "raw": "๐Ÿ’  Maintain authentic German linguistic nuances", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’  Improve cross-lingual capabilities", "raw": "๐Ÿ’  Improve cross-lingual capabilities", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’  Preserve cultural context awareness", "raw": "๐Ÿ’  Preserve cultural context awareness", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“Š Training Innovation:", "raw": "๐Ÿ“Š Training Innovation:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Our three-phase approach targeted specific layer percentages (15%, 20% and 25%) with carefully curated datasets, including:", "raw": "Our three-phase approach targeted specific layer percentages (15%, 20% and 25%) with carefully curated datasets, including:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’  Mathematics-focused content (proprietary classifier-selected)", "raw": "๐Ÿ’  Mathematics-focused content (proprietary classifier-selected)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’  High-quality German training data", "raw": "๐Ÿ’  High-quality German training data", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’  Specialized function calling datasets", "raw": "๐Ÿ’  Specialized function calling datasets", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’  Premium multilingual content", "raw": "๐Ÿ’  Premium multilingual content", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽ Community Contribution:", "raw": "๐ŸŽ Community Contribution:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We're also releasing two new datasets in a few days: ", "raw": "We're also releasing two new datasets in a few days: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "1๏ธโƒฃ SauerkrautLM-Fermented-GER-DPO: 3,300 high-quality German training samples", "raw": "1๏ธโƒฃ SauerkrautLM-Fermented-GER-DPO: 3,300 high-quality German training samples", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "2๏ธโƒฃ SauerkrautLM-Fermented-Irrelevance-GER-DPO: 2,000 specialized samples for optimized function call irrelevance handling", "raw": "2๏ธโƒฃ SauerkrautLM-Fermented-Irrelevance-GER-DPO: 2,000 specialized samples for optimized function call irrelevance handling", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Thank you to our incredible community and partners who have supported us throughout this journey. Here's to another year of AI innovation!ย ๐Ÿš€", "raw": "Thank you to our incredible community and partners who have supported us throughout this journey. Here's to another year of AI innovation!ย ๐Ÿš€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐ŸŽ‰ Celebrating One Year of #SauerkrautLM with Two Groundbreaking Releases! We're thrilled to announce the release of SauerkrautLM-v2-14b in two specialized versions: https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-SFT and https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO. Built on the robust Qwen2.5-14B foundation, these models represent a significant leap forward in multilingual AI capabilities. ๐Ÿ”ฌ Technical Breakthroughs: ๐Ÿ’  Innovative three-phase Fine-Tuning approach ๐Ÿ’  Two-step Spectrum SFT + one-step Spectrum DPO optimization phase for enhanced performance ๐Ÿ’  Balance of German and English language capabilities ๐Ÿ’  Advanced function calling - almost on par with Claude-3.5-Sonnet-20240620 ๐Ÿ‡ฉ๐Ÿ‡ช German Language Excellence: What sets this release apart is our unique achievement in simultaneously improving both German and English capabilities. Through our specialized training approach with over 1.2B tokens across two phases, we've managed to: ๐Ÿ’  Enhance German language understanding and generation (SFT Version > DPO Version) ๐Ÿ’  Maintain authentic German linguistic nuances ๐Ÿ’  Improve cross-lingual capabilities ๐Ÿ’  Preserve cultural context awareness ๐Ÿ“Š Training Innovation: Our three-phase approach targeted specific layer percentages (15%, 20% and 25%) with carefully curated datasets, including: ๐Ÿ’  Mathematics-focused content (proprietary classifier-selected) ๐Ÿ’  High-quality German training data ๐Ÿ’  Specialized function calling datasets ๐Ÿ’  Premium multilingual content ๐ŸŽ Community Contribution: We're also releasing two new datasets in a few days: 1๏ธโƒฃ SauerkrautLM-Fermented-GER-DPO: 3,300 high-quality German training samples 2๏ธโƒฃ SauerkrautLM-Fermented-Irrelevance-GER-DPO: 2,000 specialized samples for optimized function call irrelevance handling Thank you to our incredible community and partners who have supported us throughout this journey. Here's to another year of AI innovation!ย ๐Ÿš€
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2024-11-04T14:34:23.000Z
2024-11-04T14:35:08.561Z
[]
/posts/DavidGF/380609696467893
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0
878599389112746
[ { "type": "text", "value": "Early Morning Before Work Project:", "raw": "Early Morning Before Work Project:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒŒ Introducing Cascade of Semantically Integrated Layers (CaSIL): A Humorously Over-Engineered Algorithm That Actuallyโ€ฆ Works ๐Ÿคทโ€โ™‚๏ธ", "raw": "๐ŸŒŒ Introducing Cascade of Semantically Integrated Layers (CaSIL): A Humorously Over-Engineered Algorithm That Actuallyโ€ฆ Works ๐Ÿคทโ€โ™‚๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Let me introduce CaSIL โ€“ the Cascade of Semantically Integrated Layers. Imagine giving a single question the level of introspection typically reserved for philosophical debates or maybe therapy. In short, CaSIL is a pure Python reasoning algorithm that, in a series of semantically rich layers, takes any input and rebuilds it into a nuanced response thatโ€™s (surprisingly) meaningful to a human.", "raw": "Let me introduce CaSIL โ€“ the Cascade of Semantically Integrated Layers. Imagine giving a single question the level of introspection typically reserved for philosophical debates or maybe therapy. In short, CaSIL is a pure Python reasoning algorithm that, in a series of semantically rich layers, takes any input and rebuilds it into a nuanced response thatโ€™s (surprisingly) meaningful to a human.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Iโ€™ve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. Itโ€™s built without agent frameworksโ€”just good ol' Python and mathโ€”and it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Hereโ€™s a quick peek at the steps:", "raw": "Iโ€™ve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. Itโ€™s built without agent frameworksโ€”just good ol' Python and mathโ€”and it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Hereโ€™s a quick peek at the steps:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœจ How CaSIL Works:", "raw": "โœจ How CaSIL Works:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Initial Understanding: The first layer captures the basic concepts in your input, just as a warm-up.", "raw": "Initial Understanding: The first layer captures the basic concepts in your input, just as a warm-up.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Relationship Analysis: A lightweight knowledge graph (because why not?) maps out related ideas and builds interconnections.", "raw": "Relationship Analysis: A lightweight knowledge graph (because why not?) maps out related ideas and builds interconnections.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Context Integration: Adds historical or contextual knowledge, bringing a bit of depth and relevance.", "raw": "Context Integration: Adds historical or contextual knowledge, bringing a bit of depth and relevance.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Response Synthesis: Pieces it all together, aiming to produce a response that feels more like a conversation than an outdated search result.", "raw": "Response Synthesis: Pieces it all together, aiming to produce a response that feels more like a conversation than an outdated search result.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Does it work? Yes! And in record time, too. Admittedly, the code is roughโ€”two days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; itโ€™s a pure algorithm without complex dependencies, making it easy to integrate into your own LLM setups.", "raw": "Does it work? Yes! And in record time, too. Admittedly, the code is roughโ€”two days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; itโ€™s a pure algorithm without complex dependencies, making it easy to integrate into your own LLM setups.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”— Explore the repo here: ", "raw": "๐Ÿ”— Explore the repo here: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers", "resource": null, "url": null, "href": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“œ Example outputs: ", "raw": "๐Ÿ“œ Example outputs: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers/blob/main/examples.md", "resource": null, "url": null, "href": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers/blob/main/examples.md", "user": null, "lang": null, "code": null, "label": null } ]
Early Morning Before Work Project: ๐ŸŒŒ Introducing Cascade of Semantically Integrated Layers (CaSIL): A Humorously Over-Engineered Algorithm That Actuallyโ€ฆ Works ๐Ÿคทโ€โ™‚๏ธ Let me introduce CaSIL โ€“ the Cascade of Semantically Integrated Layers. Imagine giving a single question the level of introspection typically reserved for philosophical debates or maybe therapy. In short, CaSIL is a pure Python reasoning algorithm that, in a series of semantically rich layers, takes any input and rebuilds it into a nuanced response thatโ€™s (surprisingly) meaningful to a human. Iโ€™ve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. Itโ€™s built without agent frameworksโ€”just good ol' Python and mathโ€”and it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Hereโ€™s a quick peek at the steps: โœจ How CaSIL Works: Initial Understanding: The first layer captures the basic concepts in your input, just as a warm-up. Relationship Analysis: A lightweight knowledge graph (because why not?) maps out related ideas and builds interconnections. Context Integration: Adds historical or contextual knowledge, bringing a bit of depth and relevance. Response Synthesis: Pieces it all together, aiming to produce a response that feels more like a conversation than an outdated search result. Does it work? Yes! And in record time, too. Admittedly, the code is roughโ€”two days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; itโ€™s a pure algorithm without complex dependencies, making it easy to integrate into your own LLM setups. ๐Ÿ”— Explore the repo here: https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers ๐Ÿ“œ Example outputs: https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers/blob/main/examples.md
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2024-11-04T14:19:39.000Z
2024-11-04T14:19:39.731Z
[]
/posts/Severian/878599389112746
452
0
915742231261639
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I just shipped `retrain-pipelines 0.1.1` today. The doc is also pimped compared to previous release. That was clearly not mature then. I'll have to focus on another project for the next couple weeks but, anyone feel free to open issues on the GitHub repo and discuss any interest you'd have there if you will (please?) ! In the meantime, you may enjoy retrying this : https://huggingface.co/blog/Aurelien-Morgan/stateful-metaflow-on-colab
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2024-11-04T12:58:30.000Z
2024-11-04T12:58:30.527Z
[]
/posts/Aurelien-Morgan/915742231261639
446
0
169924015276572
[ { "type": "text", "value": "๐Ÿ™‹๐Ÿปโ€โ™‚๏ธhey there folks,", "raw": "๐Ÿ™‹๐Ÿปโ€โ™‚๏ธhey there folks,", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "periodic reminder : if you are experiencing โš ๏ธ500 errors โš ๏ธ or โš ๏ธ abnormal ", "raw": "periodic reminder : if you are experiencing โš ๏ธ500 errors โš ๏ธ or โš ๏ธ abnormal ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "inline_code", "value": null, "raw": "`spaces`", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": "spaces", "label": null }, { "type": "text", "value": " behavior on load or launch โš ๏ธ", "raw": " behavior on load or launch โš ๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "we have a thread ๐Ÿ‘‰๐Ÿป ", "raw": "we have a thread ๐Ÿ‘‰๐Ÿป ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://discord.com/channels/879548962464493619/1295847667515129877", "resource": null, "url": null, "href": "https://discord.com/channels/879548962464493619/1295847667515129877", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "if you can record the problem and share it there , or on the forums in your own post , please dont be shy because i'm not sure but i do think it helps ๐Ÿค—๐Ÿค—๐Ÿค—", "raw": "if you can record the problem and share it there , or on the forums in your own post , please dont be shy because i'm not sure but i do think it helps ๐Ÿค—๐Ÿค—๐Ÿค—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿ™‹๐Ÿปโ€โ™‚๏ธhey there folks, periodic reminder : if you are experiencing โš ๏ธ500 errors โš ๏ธ or โš ๏ธ abnormal `spaces` behavior on load or launch โš ๏ธ we have a thread ๐Ÿ‘‰๐Ÿป https://discord.com/channels/879548962464493619/1295847667515129877 if you can record the problem and share it there , or on the forums in your own post , please dont be shy because i'm not sure but i do think it helps ๐Ÿค—๐Ÿค—๐Ÿค—
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2024-11-04T11:41:55.000Z
2024-11-09T02:33:22.761Z
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/posts/Tonic/169924015276572
3,147
2
788696446784520
[ { "type": "text", "value": "New Style, New Mix, New Drop ๐Ÿงค", "raw": "New Style, New Mix, New Drop ๐Ÿงค", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸงจFlux LoRA DLC: ", "raw": "๐ŸงจFlux LoRA DLC: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC", "resource": { "type": "space", "id": "prithivMLmods/FLUX-LoRA-DLC", "discussionNum": null }, "url": "https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽ†Glowing-Body: ", "raw": "๐ŸŽ†Glowing-Body: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/prithivMLmods/Glowing-Body-Flux-LoRA", "resource": { "type": "model", "id": "prithivMLmods/Glowing-Body-Flux-LoRA", "discussionNum": null }, "url": "https://huggingface.co/prithivMLmods/Glowing-Body-Flux-LoRA", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽ†Electric-Blue: ", "raw": "๐ŸŽ†Electric-Blue: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/prithivMLmods/Electric-Blue-Flux-LoRA", "resource": { "type": "model", "id": "prithivMLmods/Electric-Blue-Flux-LoRA", "discussionNum": null }, "url": "https://huggingface.co/prithivMLmods/Electric-Blue-Flux-LoRA", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽ†Intense-Red: ", "raw": "๐ŸŽ†Intense-Red: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/prithivMLmods/Intense-Red-Flux-LoRA", "resource": { "type": "model", "id": "prithivMLmods/Intense-Red-Flux-LoRA", "discussionNum": null }, "url": "https://huggingface.co/prithivMLmods/Intense-Red-Flux-LoRA", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽ†Clouds-Illusion: ", "raw": "๐ŸŽ†Clouds-Illusion: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/prithivMLmods/Clouds-Illusion-Flux-LoRA", "resource": { "type": "model", "id": "prithivMLmods/Clouds-Illusion-Flux-LoRA", "discussionNum": null }, "url": "https://huggingface.co/prithivMLmods/Clouds-Illusion-Flux-LoRA", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽ†Digital-Yellow: ", "raw": "๐ŸŽ†Digital-Yellow: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/prithivMLmods/Digital-Yellow-Flux-LoRA", "resource": { "type": "model", "id": "prithivMLmods/Digital-Yellow-Flux-LoRA", "discussionNum": null }, "url": "https://huggingface.co/prithivMLmods/Digital-Yellow-Flux-LoRA", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸงจFlux LoRA Collection: ", "raw": "๐ŸงจFlux LoRA Collection: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be", "resource": { "type": "collection", "id": "prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be", "discussionNum": null }, "url": "https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@prithivMLmods", "resource": null, "url": null, "href": null, "user": "prithivMLmods", "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
New Style, New Mix, New Drop ๐Ÿงค ๐ŸงจFlux LoRA DLC: https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC ๐ŸŽ†Glowing-Body: https://huggingface.co/prithivMLmods/Glowing-Body-Flux-LoRA ๐ŸŽ†Electric-Blue: https://huggingface.co/prithivMLmods/Electric-Blue-Flux-LoRA ๐ŸŽ†Intense-Red: https://huggingface.co/prithivMLmods/Intense-Red-Flux-LoRA ๐ŸŽ†Clouds-Illusion: https://huggingface.co/prithivMLmods/Clouds-Illusion-Flux-LoRA ๐ŸŽ†Digital-Yellow: https://huggingface.co/prithivMLmods/Digital-Yellow-Flux-LoRA ๐ŸงจFlux LoRA Collection: https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be . . . @prithivMLmods
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2024-11-04T10:35:40.000Z
2024-11-05T07:56:16.114Z
[]
/posts/prithivMLmods/788696446784520
5,535
0
301983274684168
[ { "type": "text", "value": "Vector Search (most) datasets on the Hugging Face Hub ๐Ÿ”ฆ", "raw": "Vector Search (most) datasets on the Hugging Face Hub ๐Ÿ”ฆ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Powered by: Polars, DuckDB, Gradio and model2vec (lightning-fast embeddings by Stรฉphan Tulkens).", "raw": "Powered by: Polars, DuckDB, Gradio and model2vec (lightning-fast embeddings by Stรฉphan Tulkens).", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Should work fast enough for datasets up to 100K.", "raw": "Should work fast enough for datasets up to 100K.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/davidberenstein1957/vectorsearch-hub-datasets", "resource": { "type": "space", "id": "davidberenstein1957/vectorsearch-hub-datasets", "discussionNum": null }, "url": "https://huggingface.co/spaces/davidberenstein1957/vectorsearch-hub-datasets", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Vector Search (most) datasets on the Hugging Face Hub ๐Ÿ”ฆ Powered by: Polars, DuckDB, Gradio and model2vec (lightning-fast embeddings by Stรฉphan Tulkens). Should work fast enough for datasets up to 100K. https://huggingface.co/spaces/davidberenstein1957/vectorsearch-hub-datasets
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[]
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2024-11-04T10:19:03.000Z
2024-11-04T10:19:03.151Z
[]
/posts/davidberenstein1957/301983274684168
3,061
0
419590652134553
[ { "type": "text", "value": "Hello researchers! Here are scripts to generate reviews on HF Daily Papers:", "raw": "Hello researchers! Here are scripts to generate reviews on HF Daily Papers:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰ ", "raw": "๐Ÿ‘‰ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/averkij/top_papers", "resource": null, "url": null, "href": "https://github.com/averkij/top_papers", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โš™๏ธ Works on GitHub Actions", "raw": "โš™๏ธ Works on GitHub Actions", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿค– Claude, GPT-4o, FLUX", "raw": "๐Ÿค– Claude, GPT-4o, FLUX", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒ Multiple languages", "raw": "๐ŸŒ Multiple languages", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“š Classification by 38 topics (#agents, #multimodal, #plp, etc.)", "raw": "๐Ÿ“š Classification by 38 topics (#agents, #multimodal, #plp, etc.)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”บ ", "raw": "๐Ÿ”บ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://HFday.ru", "resource": null, "url": null, "href": "https://HFday.ru", "user": null, "lang": null, "code": null, "label": null } ]
Hello researchers! Here are scripts to generate reviews on HF Daily Papers: ๐Ÿ‘‰ https://github.com/averkij/top_papers โš™๏ธ Works on GitHub Actions ๐Ÿค– Claude, GPT-4o, FLUX ๐ŸŒ Multiple languages ๐Ÿ“š Classification by 38 topics (#agents, #multimodal, #plp, etc.) ๐Ÿ”บ https://HFday.ru
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2024-11-04T10:07:24.000Z
2024-11-04T10:08:40.111Z
[]
/posts/averoo/419590652134553
332
0
808388125499602
[ { "type": "text", "value": "It's work like this that in some way signal the eventual โ€œdominanceโ€ of AI over all the sciences.", "raw": "It's work like this that in some way signal the eventual โ€œdominanceโ€ of AI over all the sciences.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ€œWe train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the modelโ€™s displacement outputsโ€", "raw": "โ€œWe train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the modelโ€™s displacement outputsโ€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The emulator is capable of predicting", "raw": "The emulator is capable of predicting", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "the nonlinear displacement and velocity fields for 128^3 particles in half a second on a single GPU๐Ÿคฏ", "raw": "the nonlinear displacement and velocity fields for 128^3 particles in half a second on a single GPU๐Ÿคฏ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
It's work like this that in some way signal the eventual โ€œdominanceโ€ of AI over all the sciences. โ€œWe train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the modelโ€™s displacement outputsโ€ The emulator is capable of predicting the nonlinear displacement and velocity fields for 128^3 particles in half a second on a single GPU๐Ÿคฏ
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[]
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2024-11-04T07:12:47.000Z
2024-11-05T07:42:43.778Z
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/posts/Jaward/808388125499602
2,071
1
354334873318056
[ { "type": "text", "value": "I've released several new Hugging Face Spaces. ", "raw": "I've released several new Hugging Face Spaces. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "My primary objective is to create consistent character facial animation using image-to-image techniques:", "raw": "My primary objective is to create consistent character facial animation using image-to-image techniques:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Akjava/CreateConsistentCharacterFacialAnimationWithImg2Img", "resource": { "type": "space", "id": "Akjava/CreateConsistentCharacterFacialAnimationWithImg2Img", "discussionNum": null }, "url": "https://huggingface.co/spaces/Akjava/CreateConsistentCharacterFacialAnimationWithImg2Img", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "A short-term goal is create simple talk-head animation.", "raw": "A short-term goal is create simple talk-head animation.", "resource": null, "url": null, 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"https://huggingface.co/spaces/Akjava/AIDiagramChatWithVoice-FaceCharacter", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "[Space]", "raw": "[Space]", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- GPU tools", "raw": "- GPU tools", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Flux1-schnell img2img", "raw": "Flux1-schnell img2img", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Akjava/flux1-schnell-img2img", "resource": { "type": "space", "id": "Akjava/flux1-schnell-img2img", "discussionNum": null }, "url": "https://huggingface.co/spaces/Akjava/flux1-schnell-img2img", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, 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"\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " - Tiny CPU tools", "raw": " - Tiny CPU tools", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "WebP-3F-TH - create webp animation from 3 images", "raw": "WebP-3F-TH - create webp animation from 3 images", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "OpenCV-Inapint - classic inpaint", "raw": "OpenCV-Inapint - classic inpaint", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Whitebalance - simple white balance", "raw": "Whitebalance - simple white balance", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Paste Image - just paste image with mask", "raw": "Paste Image - just paste image with mask", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "WebP Resize Convert - resize and convert webp-animation ", "raw": "WebP Resize Convert - resize and convert webp-animation ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
I've released several new Hugging Face Spaces. My primary objective is to create consistent character facial animation using image-to-image techniques: https://huggingface.co/spaces/Akjava/CreateConsistentCharacterFacialAnimationWithImg2Img A short-term goal is create simple talk-head animation. WebP-3-Frame-Talking-Animation https://huggingface.co/spaces/Akjava/AIDiagramChatWithVoice-FaceCharacter [Space] - GPU tools Flux1-schnell img2img https://huggingface.co/spaces/Akjava/flux1-schnell-img2img Flux1-schnell Inpaint with mask-file https://huggingface.co/spaces/Akjava/flux1-schnell-img2img - Tiny CPU tools WebP-3F-TH - create webp animation from 3 images OpenCV-Inapint - classic inpaint Whitebalance - simple white balance Paste Image - just paste image with mask WebP Resize Convert - resize and convert webp-animation
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2024-11-03T23:47:03.000Z
2024-11-03T23:47:03.131Z
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/posts/Akjava/354334873318056
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[ { "type": "text", "value": "Forget any document retrievers, use ColPali ๐Ÿ’ฅ๐Ÿ’ฅ", "raw": "Forget any document retrievers, use ColPali ๐Ÿ’ฅ๐Ÿ’ฅ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Document retrieval is done through OCR + layout detection, but you are losing a lot of information in between, stop doing that! ๐Ÿค“", "raw": "Document retrieval is done through OCR + layout detection, but you are losing a lot of information in between, stop doing that! ๐Ÿค“", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "ColPali uses a vision language model, which is better in doc understanding ๐Ÿ“‘ ", "raw": "ColPali uses a vision language model, which is better in doc understanding ๐Ÿ“‘ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "ColPali: ", "raw": "ColPali: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/vidore/colpali", "resource": { "type": "model", "id": "vidore/colpali", "discussionNum": null }, "url": "https://huggingface.co/vidore/colpali", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " (mit license!)", "raw": " (mit license!)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Blog post: ", "raw": "Blog post: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/manu/colpali", "resource": null, "url": null, "href": "https://huggingface.co/blog/manu/colpali", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The authors also released a new benchmark for document retrieval: ", "raw": "The authors also released a new benchmark for document retrieval: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "ViDoRe Benchmark: ", "raw": "ViDoRe Benchmark: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/vidore/vidore-benchmark-667173f98e70a1c0fa4db00d", "resource": { "type": "collection", "id": "vidore/vidore-benchmark-667173f98e70a1c0fa4db00d", "discussionNum": null }, "url": "https://huggingface.co/collections/vidore/vidore-benchmark-667173f98e70a1c0fa4db00d", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "ViDoRe Leaderboard: ", "raw": "ViDoRe Leaderboard: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/vidore/vidore-leaderboard", "resource": { "type": "space", "id": "vidore/vidore-leaderboard", "discussionNum": null }, "url": "https://huggingface.co/spaces/vidore/vidore-leaderboard", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "ColPali marries the idea of modern vision language models with retrieval ๐Ÿค", "raw": "ColPali marries the idea of modern vision language models with retrieval ๐Ÿค", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The authors apply contrastive fine-tuning to SigLIP on documents, and pool the outputs (they call it BiSigLip). Then they feed the patch embedding outputs to PaliGemma and create BiPali ๐Ÿ–‡๏ธ", "raw": "The authors apply contrastive fine-tuning to SigLIP on documents, and pool the outputs (they call it BiSigLip). Then they feed the patch embedding outputs to PaliGemma and create BiPali ๐Ÿ–‡๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "BiPali natively supports image patch embeddings to an LLM, which enables leveraging the ColBERT-like late interaction computations between text tokens and image patches (hence the name ColPali!) ๐Ÿคฉ", "raw": "BiPali natively supports image patch embeddings to an LLM, which enables leveraging the ColBERT-like late interaction computations between text tokens and image patches (hence the name ColPali!) ๐Ÿคฉ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The authors created the ViDoRe benchmark by collecting PDF documents and generate queries from Claude-3 Sonnet. ", "raw": "The authors created the ViDoRe benchmark by collecting PDF documents and generate queries from Claude-3 Sonnet. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "ColPali seems to be the most performant model on ViDoRe. Not only this, but is way faster than traditional PDF parsers too!", "raw": "ColPali seems to be the most performant model on ViDoRe. Not only this, but is way faster than traditional PDF parsers too!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Forget any document retrievers, use ColPali ๐Ÿ’ฅ๐Ÿ’ฅ Document retrieval is done through OCR + layout detection, but you are losing a lot of information in between, stop doing that! ๐Ÿค“ ColPali uses a vision language model, which is better in doc understanding ๐Ÿ“‘ ColPali: https://huggingface.co/vidore/colpali (mit license!) Blog post: https://huggingface.co/blog/manu/colpali The authors also released a new benchmark for document retrieval: ViDoRe Benchmark: https://huggingface.co/collections/vidore/vidore-benchmark-667173f98e70a1c0fa4db00d ViDoRe Leaderboard: https://huggingface.co/spaces/vidore/vidore-leaderboard ColPali marries the idea of modern vision language models with retrieval ๐Ÿค The authors apply contrastive fine-tuning to SigLIP on documents, and pool the outputs (they call it BiSigLip). Then they feed the patch embedding outputs to PaliGemma and create BiPali ๐Ÿ–‡๏ธ BiPali natively supports image patch embeddings to an LLM, which enables leveraging the ColBERT-like late interaction computations between text tokens and image patches (hence the name ColPali!) ๐Ÿคฉ The authors created the ViDoRe benchmark by collecting PDF documents and generate queries from Claude-3 Sonnet. ColPali seems to be the most performant model on ViDoRe. Not only this, but is way faster than traditional PDF parsers too!
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2024-07-10T12:06:54.000Z
2024-07-10T12:06:54.657Z
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/posts/merve/908031542607671
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[ { "type": "text", "value": "๐Ÿ”ฅ๐ŸŽญ๐ŸŒŸ New Research Alert - LivePortrait (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿ”ฅ", "raw": "๐Ÿ”ฅ๐ŸŽญ๐ŸŒŸ New Research Alert - LivePortrait (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿ”ฅ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“„ Title: LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control ๐Ÿ”", "raw": "๐Ÿ“„ Title: LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control ๐Ÿ”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ Description: LivePortrait is an efficient video-driven portrait animation framework that uses implicit keypoints and stitching/retargeting modules to generate high-quality, controllable animations from a single source image.", "raw": "๐Ÿ“ Description: LivePortrait is an efficient video-driven portrait animation framework that uses implicit keypoints and stitching/retargeting modules to generate high-quality, controllable animations from a single source image.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, 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null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/KwaiVGI/LivePortrait", "resource": { "type": "model", "id": "KwaiVGI/LivePortrait", "discussionNum": null }, "url": "https://huggingface.co/KwaiVGI/LivePortrait", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ CVPR-2023-24-Papers: ", "raw": "๐Ÿš€ CVPR-2023-24-Papers: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/DmitryRyumin/CVPR-2023-24-Papers", "resource": null, "url": null, "href": "https://github.com/DmitryRyumin/CVPR-2023-24-Papers", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ WACV-2024-Papers: ", "raw": "๐Ÿš€ WACV-2024-Papers: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/DmitryRyumin/WACV-2024-Papers", "resource": null, "url": null, "href": "https://github.com/DmitryRyumin/WACV-2024-Papers", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ ICCV-2023-Papers: ", "raw": "๐Ÿš€ ICCV-2023-Papers: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/DmitryRyumin/ICCV-2023-Papers", "resource": null, "url": null, "href": "https://github.com/DmitryRyumin/ICCV-2023-Papers", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "raw": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "resource": { "type": "space", "id": "DmitryRyumin/NewEraAI-Papers", "discussionNum": null }, "url": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " curated by ", "raw": " curated by ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@DmitryRyumin", "resource": null, "url": null, "href": null, "user": "DmitryRyumin", "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ Added to the Avatars Collection: ", "raw": "๐Ÿš€ Added to the Avatars Collection: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "resource": { "type": "collection", "id": "DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "discussionNum": null }, "url": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ” Keywords: #LivePortrait #PortraitAnimation #ComputerVision #MachineLearning #DeepLearning #ComputerGraphics #FacialAnimation #GenerativeAI #RealTimeRendering #AI", "raw": "๐Ÿ” Keywords: #LivePortrait #PortraitAnimation #ComputerVision #MachineLearning #DeepLearning #ComputerGraphics #FacialAnimation #GenerativeAI #RealTimeRendering #AI", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿ”ฅ๐ŸŽญ๐ŸŒŸ New Research Alert - LivePortrait (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿ”ฅ ๐Ÿ“„ Title: LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control ๐Ÿ” ๐Ÿ“ Description: LivePortrait is an efficient video-driven portrait animation framework that uses implicit keypoints and stitching/retargeting modules to generate high-quality, controllable animations from a single source image. ๐Ÿ‘ฅ Authors: @cleardusk, Dingyun Zhang, Xiaoqiang Liu, Zhizhou Zhong, Yuan Zhang, Pengfei Wan, and Di Zhang ๐Ÿค— Demo: https://huggingface.co/spaces/KwaiVGI/LivePortrait ๐Ÿ“„ Paper: https://huggingface.co/papers/2407.03168 ๐ŸŒ Github Page: https://liveportrait.github.io/ ๐Ÿ“ Repository: https://github.com/KwaiVGI/LivePortrait ๐Ÿ”ฅ Model ๐Ÿค–: https://huggingface.co/KwaiVGI/LivePortrait ๐Ÿš€ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers ๐Ÿš€ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers ๐Ÿš€ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers ๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin ๐Ÿš€ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36 ๐Ÿ” Keywords: #LivePortrait #PortraitAnimation #ComputerVision #MachineLearning #DeepLearning #ComputerGraphics #FacialAnimation #GenerativeAI #RealTimeRendering #AI
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2024-07-10T11:17:58.000Z
2024-07-10T11:17:58.214Z
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/posts/DmitryRyumin/181465871908039
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[ { "type": "text", "value": "Hello.", "raw": "Hello.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I'm excited to announce that 'LivePortrait' has been released, and we've developed an advanced application service using it. We've made this service available for free on our Discord server (", "raw": "I'm excited to announce that 'LivePortrait' has been released, and we've developed an advanced application service using it. We've made this service available for free on our Discord server (", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://discord.gg/openfreeai", "resource": null, "url": null, "href": "https://discord.gg/openfreeai", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "). To achieve this, we've deployed four Nvidia H100 GPUs and optimized our code for multi-GPU processing.", "raw": "). To achieve this, we've deployed four Nvidia H100 GPUs and optimized our code for multi-GPU processing.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "On our Discord server, we've named this channel \"Mimic Face.\" This incredible free service allows you to create an interactive video in just a few seconds based on your photo. Simply upload a photo and select a face video you want to mimic. The AI will then replicate your face, mimicking gestures, expressions, mouth movements, eye movements, and even blinking naturally.", "raw": "On our Discord server, we've named this channel \"Mimic Face.\" This incredible free service allows you to create an interactive video in just a few seconds based on your photo. Simply upload a photo and select a face video you want to mimic. The AI will then replicate your face, mimicking gestures, expressions, mouth movements, eye movements, and even blinking naturally.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The process is extremely simple, and the results are impressively realistic. Experience this fantastic service for yourself, and stay tuned for more exciting AI services we plan to introduce in the future.", "raw": "The process is extremely simple, and the results are impressively realistic. Experience this fantastic service for yourself, and stay tuned for more exciting AI services we plan to introduce in the future.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Open Service link: ", "raw": "Open Service link: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://discord.gg/openfreeai", "resource": null, "url": null, "href": "https://discord.gg/openfreeai", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Channel Direct Link: ", "raw": "Channel Direct Link: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://discord.com/channels/1228254992729767996/1259659402118692896", "resource": null, "url": null, "href": "https://discord.com/channels/1228254992729767996/1259659402118692896", "user": null, "lang": null, "code": null, "label": null } ]
Hello. I'm excited to announce that 'LivePortrait' has been released, and we've developed an advanced application service using it. We've made this service available for free on our Discord server (https://discord.gg/openfreeai). To achieve this, we've deployed four Nvidia H100 GPUs and optimized our code for multi-GPU processing. On our Discord server, we've named this channel "Mimic Face." This incredible free service allows you to create an interactive video in just a few seconds based on your photo. Simply upload a photo and select a face video you want to mimic. The AI will then replicate your face, mimicking gestures, expressions, mouth movements, eye movements, and even blinking naturally. The process is extremely simple, and the results are impressively realistic. Experience this fantastic service for yourself, and stay tuned for more exciting AI services we plan to introduce in the future. Open Service link: https://discord.gg/openfreeai Channel Direct Link: https://discord.com/channels/1228254992729767996/1259659402118692896
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2024-07-10T03:23:23.000Z
2024-07-13T06:01:46.565Z
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/posts/fantaxy/374350998774778
3,535
3
284569204796434
[ { "type": "text", "value": "# LivePortrait 1-Click Installers and full tutorials for Windows and Cloud (useful for Mac users) (Massed Compute, RunPod and a free Kaggle Account)", "raw": "# LivePortrait 1-Click Installers and full tutorials for Windows and Cloud (useful for Mac users) (Massed Compute, RunPod and a free Kaggle Account)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I know there are a lot of experts around here that can install and use easily. But I have prepared solid tutorials for newbies and shown how to use this amazing top quality app LivePortrait. I have to say congrats to the developers.", "raw": "I know there are a lot of experts around here that can install and use easily. But I have prepared solid tutorials for newbies and shown how to use this amazing top quality app LivePortrait. I have to say congrats to the developers.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I am also a researcher you can see my LinkedIn profile here but recently I am shifted into AI lectures : ", "raw": "I am also a researcher you can see my LinkedIn profile here but recently I am shifted into AI lectures : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.linkedin.com/in/furkangozukara/", "resource": null, "url": null, "href": "https://www.linkedin.com/in/furkangozukara/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Both Windows and Cloud tutorial has manually written (100% accurate) captions / subtitles. Also both have manually written by me very detailed video chapters.", "raw": "Both Windows and Cloud tutorial has manually written (100% accurate) captions / subtitles. Also both have manually written by me very detailed video chapters.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Windows LivePortrait Tutorial : ", "raw": "Windows LivePortrait Tutorial : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://youtu.be/FPtpNrmuwXk", "resource": null, "url": null, "href": "https://youtu.be/FPtpNrmuwXk", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Cloud LivePortrait Tutorial : Massed Compute, RunPod & Kaggle : ", "raw": "Cloud LivePortrait Tutorial : Massed Compute, RunPod & Kaggle : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://youtu.be/wG7oPp01COg", "resource": null, "url": null, "href": "https://youtu.be/wG7oPp01COg", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "## Windows LivePortrait Tutorial Video Chapters", "raw": "## Windows LivePortrait Tutorial Video Chapters", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 0:00 Introduction to LivePortrait: A cutting-edge open-source application for image-to-animation conversion", "raw": "- 0:00 Introduction to LivePortrait: A cutting-edge open-source application for image-to-animation conversion", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 2:20 Step-by-step guide for downloading and installing the LivePortrait Gradio application on your device", "raw": "- 2:20 Step-by-step guide for downloading and installing the LivePortrait Gradio application on your device", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 3:27 System requirements and installation process for LivePortrait", "raw": "- 3:27 System requirements and installation process for LivePortrait", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 4:07 Verifying the successful installation of required components", "raw": "- 4:07 Verifying the successful installation of required components", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 5:02 Confirming installation completion and preserving installation logs", "raw": "- 5:02 Confirming installation completion and preserving installation logs", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 5:37 Initiating the LivePortrait application post-installation", "raw": "- 5:37 Initiating the LivePortrait application post-installation", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "....", "raw": "....", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
# LivePortrait 1-Click Installers and full tutorials for Windows and Cloud (useful for Mac users) (Massed Compute, RunPod and a free Kaggle Account) I know there are a lot of experts around here that can install and use easily. But I have prepared solid tutorials for newbies and shown how to use this amazing top quality app LivePortrait. I have to say congrats to the developers. I am also a researcher you can see my LinkedIn profile here but recently I am shifted into AI lectures : https://www.linkedin.com/in/furkangozukara/ Both Windows and Cloud tutorial has manually written (100% accurate) captions / subtitles. Also both have manually written by me very detailed video chapters. Windows LivePortrait Tutorial : https://youtu.be/FPtpNrmuwXk Cloud LivePortrait Tutorial : Massed Compute, RunPod & Kaggle : https://youtu.be/wG7oPp01COg ## Windows LivePortrait Tutorial Video Chapters - 0:00 Introduction to LivePortrait: A cutting-edge open-source application for image-to-animation conversion - 2:20 Step-by-step guide for downloading and installing the LivePortrait Gradio application on your device - 3:27 System requirements and installation process for LivePortrait - 4:07 Verifying the successful installation of required components - 5:02 Confirming installation completion and preserving installation logs - 5:37 Initiating the LivePortrait application post-installation ....
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[]
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2024-07-10T02:48:34.000Z
2024-07-10T02:51:07.773Z
[]
/posts/MonsterMMORPG/284569204796434
861
0
723267607752488
[ { "type": "text", "value": "๐Ÿšจ New Release: ultralytics8.2.51", "raw": "๐Ÿšจ New Release: ultralytics8.2.51", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸบLive Space : ", "raw": "๐ŸบLive Space : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/prithivMLmods/YOLO-VIDEO", "resource": { "type": "space", "id": "prithivMLmods/YOLO-VIDEO", "discussionNum": null }, "url": "https://huggingface.co/spaces/prithivMLmods/YOLO-VIDEO", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " , Duplicate the Space to avoid queuing issues.", "raw": " , Duplicate the Space to avoid queuing issues.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸบT4 Colab : ", "raw": "๐ŸบT4 Colab : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/1BKgFUfk2Me1cSPFmbtZSVCn_4cYImPO-?au", "resource": null, "url": null, "href": "https://colab.research.google.com/drive/1BKgFUfk2Me1cSPFmbtZSVCn_4cYImPO-?au", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰๐ŸปFor HPC, use A100/T4 under controlled conditions.", "raw": "๐Ÿ‘‰๐ŸปFor HPC, use A100/T4 under controlled conditions.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰๐ŸปSpeed Estimation, Object Counting, Distance Calculation, Workout Monitoring, Heatmaps,etc.", "raw": "๐Ÿ‘‰๐ŸปSpeed Estimation, Object Counting, Distance Calculation, Workout Monitoring, Heatmaps,etc.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Ultralytics dropped the YOLOv8 - #Ultralytics 8.2.51 ๐Ÿ”ฅ, YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.", "raw": "Ultralytics dropped the YOLOv8 - #Ultralytics 8.2.51 ๐Ÿ”ฅ, YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”— ", "raw": "๐Ÿ”— ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://pypi.org/project/ultralytics/8.2.58/", "resource": null, "url": null, "href": "https://pypi.org/project/ultralytics/8.2.58/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€More Features You can try:", "raw": "๐Ÿš€More Features You can try:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Classes selection support added", "raw": "โœ… Classes selection support added", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Live FPS display in the sidebar ", "raw": "โœ… Live FPS display in the sidebar ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Webcam and video support added", "raw": "โœ… Webcam and video support added", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Confidence and NMS threshold option to modify.", "raw": "โœ… Confidence and NMS threshold option to modify.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Segmentation, detection, and pose models support added.", "raw": "โœ… Segmentation, detection, and pose models support added.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ™€Ultralytics Live inference: ", "raw": "๐Ÿ™€Ultralytics Live inference: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://docs.ultralytics.com/guides/streamlit-live-inference/", "resource": null, "url": null, "href": "https://docs.ultralytics.com/guides/streamlit-live-inference/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "code_fence", "value": null, "raw": "```\nfrom ultralytics import solutions\nsolutions.inference()\n### Make sure to run the file using command `streamlit run <file-name.py>`\n\n```", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": "from ultralytics import solutions\nsolutions.inference()\n### Make sure to run the file using command `streamlit run <file-name.py>`", "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โšกyolo streamlit-predict", "raw": "โšกyolo streamlit-predict", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰๐ŸปAdvantages of Live Inference", "raw": "๐Ÿ‘‰๐ŸปAdvantages of Live Inference", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " โ˜‘๏ธ Seamless Real-Time Object Detection: Streamlit combined with YOLOv8 enables real-time object detection directly from your webcam feed. This allows for immediate analysis and insights, making it ideal for applications requiring instant feedback.", "raw": " โ˜‘๏ธ Seamless Real-Time Object Detection: Streamlit combined with YOLOv8 enables real-time object detection directly from your webcam feed. This allows for immediate analysis and insights, making it ideal for applications requiring instant feedback.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " โ˜‘๏ธEfficient Resource Utilization: YOLOv8 optimized algorithm ensure high-speed processing with minimal computational resources.", "raw": " โ˜‘๏ธEfficient Resource Utilization: YOLOv8 optimized algorithm ensure high-speed processing with minimal computational resources.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ™€Ultralytics feature Models: ", "raw": "๐Ÿ™€Ultralytics feature Models: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://docs.ultralytics.com/models/", "resource": null, "url": null, "href": "https://docs.ultralytics.com/models/", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ", Ultralytics new Solutions: ", "raw": ", Ultralytics new Solutions: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://docs.ultralytics.com/solutions/", "resource": null, "url": null, "href": "https://docs.ultralytics.com/solutions/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰๐ŸปOfficial Documentation:", "raw": "๐Ÿ‘‰๐ŸปOfficial Documentation:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " Ultralytics YOLOv8 Documentation: Refer to the official YOLOv8 documentation for comprehensive guides and insights on various computer vision tasks and projects. ๐Ÿ”— ", "raw": " Ultralytics YOLOv8 Documentation: Refer to the official YOLOv8 documentation for comprehensive guides and insights on various computer vision tasks and projects. ๐Ÿ”— ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://docs.ultralytics.com/", "resource": null, "url": null, "href": "https://docs.ultralytics.com/", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿšจ New Release: ultralytics8.2.51 ๐ŸบLive Space : https://huggingface.co/spaces/prithivMLmods/YOLO-VIDEO , Duplicate the Space to avoid queuing issues. ๐ŸบT4 Colab : https://colab.research.google.com/drive/1BKgFUfk2Me1cSPFmbtZSVCn_4cYImPO-?au ๐Ÿ‘‰๐ŸปFor HPC, use A100/T4 under controlled conditions. ๐Ÿ‘‰๐ŸปSpeed Estimation, Object Counting, Distance Calculation, Workout Monitoring, Heatmaps,etc. Ultralytics dropped the YOLOv8 - #Ultralytics 8.2.51 ๐Ÿ”ฅ, YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks. ๐Ÿ”— https://pypi.org/project/ultralytics/8.2.58/ ๐Ÿš€More Features You can try: โœ… Classes selection support added โœ… Live FPS display in the sidebar โœ… Webcam and video support added โœ… Confidence and NMS threshold option to modify. โœ… Segmentation, detection, and pose models support added. ๐Ÿ™€Ultralytics Live inference: https://docs.ultralytics.com/guides/streamlit-live-inference/ ``` from ultralytics import solutions solutions.inference() ### Make sure to run the file using command `streamlit run <file-name.py>` ``` โšกyolo streamlit-predict ๐Ÿ‘‰๐ŸปAdvantages of Live Inference โ˜‘๏ธ Seamless Real-Time Object Detection: Streamlit combined with YOLOv8 enables real-time object detection directly from your webcam feed. This allows for immediate analysis and insights, making it ideal for applications requiring instant feedback. โ˜‘๏ธEfficient Resource Utilization: YOLOv8 optimized algorithm ensure high-speed processing with minimal computational resources. ๐Ÿ™€Ultralytics feature Models: https://docs.ultralytics.com/models/, Ultralytics new Solutions: https://docs.ultralytics.com/solutions/ ๐Ÿ‘‰๐ŸปOfficial Documentation: Ultralytics YOLOv8 Documentation: Refer to the official YOLOv8 documentation for comprehensive guides and insights on various computer vision tasks and projects. ๐Ÿ”— https://docs.ultralytics.com/
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2024-07-10T01:05:41.000Z
2024-07-22T05:34:11.880Z
[]
/posts/prithivMLmods/723267607752488
3,266
0
783317433513561
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Check @lllyasviel Latest research for making videos of still images using hand drawing keyframes: https://lllyasviel.github.io/pages/paints_undo/ I imported his work to gradio space here https://huggingface.co/spaces/MohamedRashad/PaintsUndo
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2024-07-09T21:56:32.000Z
2024-07-09T21:56:32.747Z
[]
/posts/MohamedRashad/783317433513561
2,597
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291772901595312
[ { "type": "text", "value": "๐Ÿ“ข Exciting News! Our latest paper \"ChartGemma\" is out! ๐Ÿ“Š", "raw": "๐Ÿ“ข Exciting News! Our latest paper \"ChartGemma\" is out! ๐Ÿ“Š", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿงต1/3: ChartGemma overcomes existing chart models key limitations that rely too much on data tables. Instead, it is trained on data generated directly from chart images, capturing crucial visual trends๐Ÿ“ธ๐Ÿ”", "raw": "๐Ÿงต1/3: ChartGemma overcomes existing chart models key limitations that rely too much on data tables. Instead, it is trained on data generated directly from chart images, capturing crucial visual trends๐Ÿ“ธ๐Ÿ”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿงต2/3: ChartGemma builds upon PaliGemma from Google Research and is fine-tuned on a high-quality visual instruction tuning dataset generated from Gemini Flash 1.5. ๐ŸŒŸ๐Ÿ“Š", "raw": "๐Ÿงต2/3: ChartGemma builds upon PaliGemma from Google Research and is fine-tuned on a high-quality visual instruction tuning dataset generated from Gemini Flash 1.5. ๐ŸŒŸ๐Ÿ“Š", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿงต3/3: Achieves state-of-the-art results in chart summarization, question answering, and fact-checking tasks. ๐Ÿ…๐Ÿ“Š It can also generate more accurate and realistic chart summaries. ๐Ÿ“๐Ÿ”", "raw": "๐Ÿงต3/3: Achieves state-of-the-art results in chart summarization, question answering, and fact-checking tasks. ๐Ÿ…๐Ÿ“Š It can also generate more accurate and realistic chart summaries. ๐Ÿ“๐Ÿ”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Our model and data are publicly available. We also have a cool web demo. Check it out! ๐Ÿš€", "raw": "Our model and data are publicly available. We also have a cool web demo. Check it out! ๐Ÿš€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Demo: ", "raw": "Demo: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/ahmed-masry/ChartGemma", "resource": { "type": "space", "id": "ahmed-masry/ChartGemma", "discussionNum": null }, "url": "https://huggingface.co/spaces/ahmed-masry/ChartGemma", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Code: ", "raw": "Code: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/vis-nlp/ChartGemma", "resource": null, "url": null, "href": "https://github.com/vis-nlp/ChartGemma", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2407.04172", "resource": { "type": "paper", "id": "2407.04172", "discussionNum": null }, "url": "https://huggingface.co/papers/2407.04172", "href": null, "user": null, "lang": null, "code": null, "label": "ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild (2407.04172)" }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿ“ข Exciting News! Our latest paper "ChartGemma" is out! ๐Ÿ“Š ๐Ÿงต1/3: ChartGemma overcomes existing chart models key limitations that rely too much on data tables. Instead, it is trained on data generated directly from chart images, capturing crucial visual trends๐Ÿ“ธ๐Ÿ” ๐Ÿงต2/3: ChartGemma builds upon PaliGemma from Google Research and is fine-tuned on a high-quality visual instruction tuning dataset generated from Gemini Flash 1.5. ๐ŸŒŸ๐Ÿ“Š ๐Ÿงต3/3: Achieves state-of-the-art results in chart summarization, question answering, and fact-checking tasks. ๐Ÿ…๐Ÿ“Š It can also generate more accurate and realistic chart summaries. ๐Ÿ“๐Ÿ” Our model and data are publicly available. We also have a cool web demo. Check it out! ๐Ÿš€ Demo: https://huggingface.co/spaces/ahmed-masry/ChartGemma Code: https://github.com/vis-nlp/ChartGemma Paper: https://huggingface.co/papers/2407.04172
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2024-07-09T21:42:57.000Z
2024-07-09T21:42:57.877Z
[]
/posts/ahmed-masry/291772901595312
3,320
0
738798942111679
[ { "type": "text", "value": "How's 2024 been so far? ๐ŸŽ‰ Well, we've got the data to find out!", "raw": "How's 2024 been so far? ๐ŸŽ‰ Well, we've got the data to find out!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "New dataset just dropped on the Hub: 12.5 million Reddit comments from 2024! ๐Ÿ“Š๐Ÿ—ฃ๏ธ", "raw": "New dataset just dropped on the Hub: 12.5 million Reddit comments from 2024! ๐Ÿ“Š๐Ÿ—ฃ๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Features:", "raw": "Features:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Personal info anonymized ๐Ÿ•ต๏ธโ€โ™€๏ธ", "raw": "- Personal info anonymized ๐Ÿ•ต๏ธโ€โ™€๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Language detection ๐ŸŒ", "raw": "- Language detection ๐ŸŒ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Token count ๐Ÿ”ข", "raw": "- Token count ๐Ÿ”ข", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- NSFW filtering ๐Ÿšซ", "raw": "- NSFW filtering ๐Ÿšซ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Most popular post? Classic AITA drama: \"AITA for 'ruining Christmas' and being upset the only gifts I got from my family were 'joke gifts'\" ๐ŸŽ„๐Ÿ˜…", "raw": "Most popular post? Classic AITA drama: \"AITA for 'ruining Christmas' and being upset the only gifts I got from my family were 'joke gifts'\" ๐ŸŽ„๐Ÿ˜…", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Some things never change, huh?", "raw": "Some things never change, huh?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Dive in: ", "raw": "Dive in: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/OpenCo7/UpVoteWeb", "resource": { "type": "dataset", "id": "OpenCo7/UpVoteWeb", "discussionNum": null }, "url": "https://huggingface.co/datasets/OpenCo7/UpVoteWeb", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
How's 2024 been so far? ๐ŸŽ‰ Well, we've got the data to find out! New dataset just dropped on the Hub: 12.5 million Reddit comments from 2024! ๐Ÿ“Š๐Ÿ—ฃ๏ธ Features: - Personal info anonymized ๐Ÿ•ต๏ธโ€โ™€๏ธ - Language detection ๐ŸŒ - Token count ๐Ÿ”ข - NSFW filtering ๐Ÿšซ Most popular post? Classic AITA drama: "AITA for 'ruining Christmas' and being upset the only gifts I got from my family were 'joke gifts'" ๐ŸŽ„๐Ÿ˜… Some things never change, huh? Dive in: https://huggingface.co/datasets/OpenCo7/UpVoteWeb
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[]
[]
2024-07-09T19:57:14.000Z
2024-07-09T19:57:14.422Z
[]
/posts/fdaudens/738798942111679
554
0
385146777935493
[ { "type": "text", "value": "Just updated the Journalists on ๐Ÿค— community with two new AI tools! ๐Ÿš€๐Ÿ“Š", "raw": "Just updated the Journalists on ๐Ÿค— community with two new AI tools! ๐Ÿš€๐Ÿ“Š", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Check them out:", "raw": "Check them out:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Free video transcription & smart summary tool ", "raw": "- Free video transcription & smart summary tool ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/artificialguybr/Video-Transcription-Smart-Summary", "resource": { "type": "space", "id": "artificialguybr/Video-Transcription-Smart-Summary", "discussionNum": null }, "url": "https://huggingface.co/spaces/artificialguybr/Video-Transcription-Smart-Summary", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ChartGemma - next-level chart analysis focusing on visual trends ", "raw": "- ChartGemma - next-level chart analysis focusing on visual trends ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/ahmed-masry/ChartGemma", "resource": { "type": "space", "id": "ahmed-masry/ChartGemma", "discussionNum": null }, "url": "https://huggingface.co/spaces/ahmed-masry/ChartGemma", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Media folks: Join our community for more tools! ", "raw": "Media folks: Join our community for more tools! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/JournalistsonHF", "resource": null, "url": null, "href": "https://huggingface.co/JournalistsonHF", "user": null, "lang": null, "code": null, "label": null } ]
Just updated the Journalists on ๐Ÿค— community with two new AI tools! ๐Ÿš€๐Ÿ“Š Check them out: - Free video transcription & smart summary tool https://huggingface.co/spaces/artificialguybr/Video-Transcription-Smart-Summary - ChartGemma - next-level chart analysis focusing on visual trends https://huggingface.co/spaces/ahmed-masry/ChartGemma Media folks: Join our community for more tools! https://huggingface.co/JournalistsonHF
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2024-07-09T18:38:43.000Z
2024-07-09T18:38:43.431Z
[]
/posts/fdaudens/385146777935493
587
0
331313277694192
[ { "type": "text", "value": "Chrome's new ", "raw": "Chrome's new ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "inline_code", "value": null, "raw": "`windowโ€‹.ai`", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": "windowโ€‹.ai", "label": null }, { "type": "text", "value": " feature is going to change the web forever! ๐Ÿคฏ It allows you to run Gemini Nano, a powerful 3.25B parameter LLM, 100% locally in your browser!", "raw": " feature is going to change the web forever! ๐Ÿคฏ It allows you to run Gemini Nano, a powerful 3.25B parameter LLM, 100% locally in your browser!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We've also added experimental support to ๐Ÿค— Transformers.js!", "raw": "We've also added experimental support to ๐Ÿค— Transformers.js!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Demo: ", "raw": "- Demo: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Xenova/experimental-built-in-ai-chat", "resource": { "type": "space", "id": "Xenova/experimental-built-in-ai-chat", "discussionNum": null }, "url": "https://huggingface.co/spaces/Xenova/experimental-built-in-ai-chat", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Blog post: ", "raw": "- Blog post: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/Xenova/run-gemini-nano-in-your-browser", "resource": null, "url": null, "href": "https://huggingface.co/blog/Xenova/run-gemini-nano-in-your-browser", "user": null, "lang": null, "code": null, "label": null } ]
Chrome's new `windowโ€‹.ai` feature is going to change the web forever! ๐Ÿคฏ It allows you to run Gemini Nano, a powerful 3.25B parameter LLM, 100% locally in your browser! We've also added experimental support to ๐Ÿค— Transformers.js! - Demo: https://huggingface.co/spaces/Xenova/experimental-built-in-ai-chat - Blog post: https://huggingface.co/blog/Xenova/run-gemini-nano-in-your-browser
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2024-07-09T16:01:23.000Z
2024-09-13T15:41:18.174Z
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/posts/Xenova/331313277694192
6,066
5
408515778634277
[ { "type": "text", "value": "Hi HF community! ๐Ÿค—", "raw": "Hi HF community! ๐Ÿค—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "There's a new space out in the wild! ", "raw": "There's a new space out in the wild! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/as-cle-bert/self-reviewing-coding-assistant", "resource": { "type": "space", "id": "as-cle-bert/self-reviewing-coding-assistant", "discussionNum": null }, "url": "https://huggingface.co/spaces/as-cle-bert/self-reviewing-coding-assistant", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ๐Ÿฆœ", "raw": " ๐Ÿฆœ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It's a self-correcting and self-reviewing python coding assistant based on GPT4-o and LangChain, inspired by Codium-AI's AlphaCodium ๐Ÿ‘พ", "raw": "It's a self-correcting and self-reviewing python coding assistant based on GPT4-o and LangChain, inspired by Codium-AI's AlphaCodium ๐Ÿ‘พ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Have fun! ๐Ÿ™", "raw": "Have fun! ๐Ÿ™", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Hi HF community! ๐Ÿค— There's a new space out in the wild! https://huggingface.co/spaces/as-cle-bert/self-reviewing-coding-assistant ๐Ÿฆœ It's a self-correcting and self-reviewing python coding assistant based on GPT4-o and LangChain, inspired by Codium-AI's AlphaCodium ๐Ÿ‘พ Have fun! ๐Ÿ™
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2024-07-09T14:16:36.000Z
2024-07-09T14:16:56.569Z
[]
/posts/as-cle-bert/408515778634277
774
0
436334531685964
[ { "type": "text", "value": "One more cookbook:", "raw": "One more cookbook:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Agent for self-correcting Text-to-SQL ๐Ÿง‘โ€๐Ÿ’ป", "raw": "Agent for self-correcting Text-to-SQL ๐Ÿง‘โ€๐Ÿ’ป", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "What if the query generated by your Text-to-SQL pipeline is correct SQL but returns wrong results?", "raw": "What if the query generated by your Text-to-SQL pipeline is correct SQL but returns wrong results?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰ We need to add a critique step ", "raw": "๐Ÿ‘‰ We need to add a critique step ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… That's very simple with an agent!", "raw": "โœ… That's very simple with an agent!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Check out the notebook! ๐Ÿ‘‡", "raw": "Check out the notebook! ๐Ÿ‘‡", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/learn/cookbook/agent_text_to_sql", "resource": null, "url": null, "href": "https://huggingface.co/learn/cookbook/agent_text_to_sql", "user": null, "lang": null, "code": null, "label": null } ]
One more cookbook: Agent for self-correcting Text-to-SQL ๐Ÿง‘โ€๐Ÿ’ป What if the query generated by your Text-to-SQL pipeline is correct SQL but returns wrong results? ๐Ÿ‘‰ We need to add a critique step โœ… That's very simple with an agent! Check out the notebook! ๐Ÿ‘‡ https://huggingface.co/learn/cookbook/agent_text_to_sql
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[]
[]
2024-07-09T12:26:04.000Z
2024-07-09T12:26:04.362Z
[]
/posts/m-ric/436334531685964
859
0
218941495145739
[ { "type": "text", "value": "Is a full-featured Mac sufficient for AI and ML development compared to NVIDIA GPU systems?", "raw": "Is a full-featured Mac sufficient for AI and ML development compared to NVIDIA GPU systems?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Hello everyone,", "raw": "Hello everyone,", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We are about to make the decision to purchase a powerful Mac with maximum features for our university. This Mac will primarily serve as a development computer in the field of artificial intelligence (AI) and machine learning (ML) and will be used by a small group of users in the local network. After development, the systems will be transferred to servers that have to withstand higher loads and visitor numbers.", "raw": "We are about to make the decision to purchase a powerful Mac with maximum features for our university. This Mac will primarily serve as a development computer in the field of artificial intelligence (AI) and machine learning (ML) and will be used by a small group of users in the local network. After development, the systems will be transferred to servers that have to withstand higher loads and visitor numbers.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Planned system:", "raw": "Planned system:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Apple Mac Studio 2023 with M2 Ultra processor", "raw": "Apple Mac Studio 2023 with M2 Ultra processor", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "24-core CPU", "raw": "24-core CPU", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "76-core GPU", "raw": "76-core GPU", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "32-core NPU (neural engine) for machine learning", "raw": "32-core NPU (neural engine) for machine learning", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "128GB RAM", "raw": "128GB RAM", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "1TB HDD", "raw": "1TB HDD", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Our question to you:", "raw": "Our question to you:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Is a fully equipped Mac with the latest SoCs chips and integrated neural engines sufficient for the development of AI and ML systems?", "raw": "Is a fully equipped Mac with the latest SoCs chips and integrated neural engines sufficient for the development of AI and ML systems?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Or should we rather rely on proven Windows/Linux systems with powerful NVIDIA graphics cards?", "raw": "Or should we rather rely on proven Windows/Linux systems with powerful NVIDIA graphics cards?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We already have several NVIDIA graphics cards available at the university:", "raw": "We already have several NVIDIA graphics cards available at the university:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "NVIDIA Tesla T4", "raw": "NVIDIA Tesla T4", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "NVIDIA 2080Ti", "raw": "NVIDIA 2080Ti", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "NVIDIA 3080Ti", "raw": "NVIDIA 3080Ti", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We are particularly interested in your experiences and assessments of how the performance of the Mac compares to the GPU systems mentioned. ", "raw": "We are particularly interested in your experiences and assessments of how the performance of the Mac compares to the GPU systems mentioned. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Are there significant differences, especially in the development and training of models? ", "raw": "Are there significant differences, especially in the development and training of models? ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "What difference would you consider to be significant? ", "raw": "What difference would you consider to be significant? ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "For us, a difference of 100% or more would be considered significant.", "raw": "For us, a difference of 100% or more would be considered significant.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "In other words, computer A (Mac) takes twice as long to calculate as computer B (NVIDIA system).", "raw": "In other words, computer A (Mac) takes twice as long to calculate as computer B (NVIDIA system).", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Many thanks in advance for your answers and experience!", "raw": "Many thanks in advance for your answers and experience!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Best regards,", "raw": "Best regards,", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ€จOliver", "raw": "โ€จOliver", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Is a full-featured Mac sufficient for AI and ML development compared to NVIDIA GPU systems? Hello everyone, We are about to make the decision to purchase a powerful Mac with maximum features for our university. This Mac will primarily serve as a development computer in the field of artificial intelligence (AI) and machine learning (ML) and will be used by a small group of users in the local network. After development, the systems will be transferred to servers that have to withstand higher loads and visitor numbers. Planned system: Apple Mac Studio 2023 with M2 Ultra processor 24-core CPU 76-core GPU 32-core NPU (neural engine) for machine learning 128GB RAM 1TB HDD Our question to you: Is a fully equipped Mac with the latest SoCs chips and integrated neural engines sufficient for the development of AI and ML systems? Or should we rather rely on proven Windows/Linux systems with powerful NVIDIA graphics cards? We already have several NVIDIA graphics cards available at the university: NVIDIA Tesla T4 NVIDIA 2080Ti NVIDIA 3080Ti We are particularly interested in your experiences and assessments of how the performance of the Mac compares to the GPU systems mentioned. Are there significant differences, especially in the development and training of models? What difference would you consider to be significant? For us, a difference of 100% or more would be considered significant. In other words, computer A (Mac) takes twice as long to calculate as computer B (NVIDIA system). Many thanks in advance for your answers and experience! Best regards, โ€จOliver
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[]
[]
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2024-07-09T11:05:40.000Z
2024-07-09T11:05:40.671Z
[]
/posts/designermohr/218941495145739
566
0
555727830882351
[ { "type": "text", "value": "Hi everyone,", "raw": "Hi everyone,", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I am excited to introduce our latest work, LLaMAX. ๐Ÿ˜๐Ÿ˜๐Ÿ˜", "raw": "I am excited to introduce our latest work, LLaMAX. ๐Ÿ˜๐Ÿ˜๐Ÿ˜", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "LLaMAX is a powerful language model created specifically for multilingual scenarios. Built upon Meta's LLaMA series models, LLaMAX undergoes extensive training across more than 100 languages. ", "raw": "LLaMAX is a powerful language model created specifically for multilingual scenarios. Built upon Meta's LLaMA series models, LLaMAX undergoes extensive training across more than 100 languages. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Remarkably, it enhances its multilingual capabilities without compromising its generalization ability, surpassing existing LLMs.", "raw": "Remarkably, it enhances its multilingual capabilities without compromising its generalization ability, surpassing existing LLMs.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœจHighlights:", "raw": "โœจHighlights:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽˆ LLaMAX supports the 102 languages covered by Flores-101, and its performance in translating between low-resource languages far surpasses other decoder-only LLMs.", "raw": "๐ŸŽˆ LLaMAX supports the 102 languages covered by Flores-101, and its performance in translating between low-resource languages far surpasses other decoder-only LLMs.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽˆ Even for languages not covered in Flores-200, LLaMAX still shows significant improvements in translation performance.", "raw": "๐ŸŽˆ Even for languages not covered in Flores-200, LLaMAX still shows significant improvements in translation performance.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽˆ By performing simple SFT on English task data, LLaMAX demonstrates impressive multilingual transfer abilities in downstream tasks.", "raw": "๐ŸŽˆ By performing simple SFT on English task data, LLaMAX demonstrates impressive multilingual transfer abilities in downstream tasks.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽˆ In our paper, we discuss effective methods for enhancing the multilingual capabilities of LLMs during the continued training phase.", "raw": "๐ŸŽˆ In our paper, we discuss effective methods for enhancing the multilingual capabilities of LLMs during the continued training phase.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We welcome you to use our model and provide feedback.", "raw": "We welcome you to use our model and provide feedback.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "More Details:", "raw": "More Details:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽ‰ Code: ", "raw": "๐ŸŽ‰ Code: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/CONE-MT/LLaMAX/", "resource": null, "url": null, "href": "https://github.com/CONE-MT/LLaMAX/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽ‰ Model: ", "raw": "๐ŸŽ‰ Model: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/LLaMAX/", "resource": null, "url": null, "href": "https://huggingface.co/LLaMAX/", "user": null, "lang": null, "code": null, "label": null } ]
Hi everyone, I am excited to introduce our latest work, LLaMAX. ๐Ÿ˜๐Ÿ˜๐Ÿ˜ LLaMAX is a powerful language model created specifically for multilingual scenarios. Built upon Meta's LLaMA series models, LLaMAX undergoes extensive training across more than 100 languages. Remarkably, it enhances its multilingual capabilities without compromising its generalization ability, surpassing existing LLMs. โœจHighlights: ๐ŸŽˆ LLaMAX supports the 102 languages covered by Flores-101, and its performance in translating between low-resource languages far surpasses other decoder-only LLMs. ๐ŸŽˆ Even for languages not covered in Flores-200, LLaMAX still shows significant improvements in translation performance. ๐ŸŽˆ By performing simple SFT on English task data, LLaMAX demonstrates impressive multilingual transfer abilities in downstream tasks. ๐ŸŽˆ In our paper, we discuss effective methods for enhancing the multilingual capabilities of LLMs during the continued training phase. We welcome you to use our model and provide feedback. More Details: ๐ŸŽ‰ Code: https://github.com/CONE-MT/LLaMAX/ ๐ŸŽ‰ Model: https://huggingface.co/LLaMAX/
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2024-07-09T06:30:07.000Z
2024-07-12T06:48:44.351Z
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/posts/FeYuan/555727830882351
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[ { "type": "text", "value": "Hey everyone,", "raw": "Hey everyone,", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We're thrilled to introduce our latest project: a hand-curated list of the best-curated lists related to artificial intelligence! ๐Ÿš€", "raw": "We're thrilled to introduce our latest project: a hand-curated list of the best-curated lists related to artificial intelligence! ๐Ÿš€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Check it out here: ", "raw": "Check it out here: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/zhimin-z/awesome-awesome-artificial-intelligence", "resource": null, "url": null, "href": "https://github.com/zhimin-z/awesome-awesome-artificial-intelligence", "user": null, "lang": null, "code": null, "label": null } ]
Hey everyone, We're thrilled to introduce our latest project: a hand-curated list of the best-curated lists related to artificial intelligence! ๐Ÿš€ Check it out here: https://github.com/zhimin-z/awesome-awesome-artificial-intelligence
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2024-07-09T04:43:07.000Z
2024-07-09T04:43:07.361Z
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/posts/zhiminy/753573139152748
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[ { "type": "text", "value": "๐Ÿ“Excited to make public a series of checkpoints !", "raw": "๐Ÿ“Excited to make public a series of checkpoints !", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Final checkpoints after self-training with ENVISIONS framework ", "raw": "- Final checkpoints after self-training with ENVISIONS framework ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Cover math, logic, and agent domains", "raw": "- Cover math, logic, and agent domains", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Include 7B / 13B ", "raw": "- Include 7B / 13B ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“• Check our paper: ", "raw": "๐Ÿ“• Check our paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Title: Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models", "raw": "Title: Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Link: ", "raw": "Link: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2406.11736", "resource": null, "url": null, "href": "https://arxiv.org/abs/2406.11736", "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿ“Excited to make public a series of checkpoints ! - Final checkpoints after self-training with ENVISIONS framework - Cover math, logic, and agent domains - Include 7B / 13B ๐Ÿ“• Check our paper: Title: Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models Link: https://arxiv.org/abs/2406.11736
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2024-07-09T03:23:51.000Z
2024-07-10T07:48:57.012Z
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/posts/Symbol-LLM/582923465870226
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Please, help with is!!!! "You have exceeded your GPU quota... " I have paid the Pro service of 9 Euros per month, and everything remains the same, I barely do four tasks and I get the restriction message that I have to wait many minutes or more than an hour. So why have I paid the 9 Euros a month, if everything remains the same, with the same restrictions as if it were a free account? I have written, but they do not respond to me. Please help me.
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2024-07-08T22:15:21.000Z
2024-07-13T15:05:57.147Z
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/posts/GeorgeosDiazMontexano/540200274459008
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[ { "type": "text", "value": "Very cool dataset for journalists and historians just dropped: 2.7 million unique public domain U.S. news wire articles (1878-1977) ๐Ÿ“ฐ๐Ÿ•ฐ๏ธ", "raw": "Very cool dataset for journalists and historians just dropped: 2.7 million unique public domain U.S. news wire articles (1878-1977) ๐Ÿ“ฐ๐Ÿ•ฐ๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This is a goldmine for tracking historical events & newspaper coverage trends! An example? If we still wonder whether gender diversity in the media is important... \"Only 4.6% of disambiguated entity mentions refer to women, and the most mentioned woman is Golda Meir.\"", "raw": "This is a goldmine for tracking historical events & newspaper coverage trends! An example? If we still wonder whether gender diversity in the media is important... \"Only 4.6% of disambiguated entity mentions refer to women, and the most mentioned woman is Golda Meir.\"", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Bonus:", "raw": "Bonus:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Locations in these articles are georeferenced", "raw": "- Locations in these articles are georeferenced", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Topics are tagged using customized neural topic classification", "raw": "- Topics are tagged using customized neural topic classification", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Named entities are recognized,", "raw": "- Named entities are recognized,", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Individuals are disambiguated to Wikipedia using a novel entity disambiguation model ", "raw": "- Individuals are disambiguated to Wikipedia using a novel entity disambiguation model ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Anyone thinking of cool AI projects with this data? Maybe tracking the spread of news stories over time & space?", "raw": "Anyone thinking of cool AI projects with this data? Maybe tracking the spread of news stories over time & space?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐†๐จ ๐๐ž๐ž๐ฉ๐ž๐ซ", "raw": "๐†๐จ ๐๐ž๐ž๐ฉ๐ž๐ซ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰ Digg into the dataset: ", "raw": "๐Ÿ‘‰ Digg into the dataset: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/dell-research-harvard/newswire", "resource": { "type": "dataset", "id": "dell-research-harvard/newswire", "discussionNum": null }, "url": "https://huggingface.co/datasets/dell-research-harvard/newswire", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ๐Ÿ‘‰ Read the paper: ", "raw": " ๐Ÿ‘‰ Read the paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2406.09490", "resource": { "type": "paper", "id": "2406.09490", "discussionNum": null }, "url": "https://huggingface.co/papers/2406.09490", "href": null, "user": null, "lang": null, "code": null, "label": "Newswire: A Large-Scale Structured Database of a Century of Historical\n News (2406.09490)" } ]
Very cool dataset for journalists and historians just dropped: 2.7 million unique public domain U.S. news wire articles (1878-1977) ๐Ÿ“ฐ๐Ÿ•ฐ๏ธ This is a goldmine for tracking historical events & newspaper coverage trends! An example? If we still wonder whether gender diversity in the media is important... "Only 4.6% of disambiguated entity mentions refer to women, and the most mentioned woman is Golda Meir." Bonus: - Locations in these articles are georeferenced - Topics are tagged using customized neural topic classification - Named entities are recognized, - Individuals are disambiguated to Wikipedia using a novel entity disambiguation model Anyone thinking of cool AI projects with this data? Maybe tracking the spread of news stories over time & space? ๐†๐จ ๐๐ž๐ž๐ฉ๐ž๐ซ ๐Ÿ‘‰ Digg into the dataset: https://huggingface.co/datasets/dell-research-harvard/newswire ๐Ÿ‘‰ Read the paper: https://huggingface.co/papers/2406.09490
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2024-07-08T19:46:36.000Z
2024-07-08T19:46:36.578Z
[]
/posts/fdaudens/248827863204916
2,042
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583121742800907
[ { "type": "text", "value": "Running billion parameter models, sometimes we forget what it all is! ๐Ÿค”๐Ÿ’ก", "raw": "Running billion parameter models, sometimes we forget what it all is! ๐Ÿค”๐Ÿ’ก", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Matrix multiplication ๐Ÿงฎโœจ", "raw": "Matrix multiplication ๐Ÿงฎโœจ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "While there are multiple plays on memory management and caching to speed it up! ๐ŸŽ๏ธ๐Ÿ’พโšก", "raw": "While there are multiple plays on memory management and caching to speed it up! ๐ŸŽ๏ธ๐Ÿ’พโšก", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The naive way of Matrix multiplication becomes even more fascinating the bigger these models get! ๐Ÿคฏ๐Ÿ“ˆ", "raw": "The naive way of Matrix multiplication becomes even more fascinating the bigger these models get! ๐Ÿคฏ๐Ÿ“ˆ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "QKV for the win! ๐Ÿ†๐Ÿ”‘๐Ÿ“š", "raw": "QKV for the win! ๐Ÿ†๐Ÿ”‘๐Ÿ“š", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "GitHub: ", "raw": "GitHub: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/wentasah/mmul-anim", "resource": null, "url": null, "href": "https://github.com/wentasah/mmul-anim", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Slides: ", "raw": "Slides: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://cw.fel.cvut.cz/wiki/_media/courses/b4m36esw/esw09_2019.pdf", "resource": null, "url": null, "href": "https://cw.fel.cvut.cz/wiki/_media/courses/b4m36esw/esw09_2019.pdf", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ๐Ÿ“‘๐ŸŽ“", "raw": " ๐Ÿ“‘๐ŸŽ“", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Running billion parameter models, sometimes we forget what it all is! ๐Ÿค”๐Ÿ’ก Matrix multiplication ๐Ÿงฎโœจ While there are multiple plays on memory management and caching to speed it up! ๐ŸŽ๏ธ๐Ÿ’พโšก The naive way of Matrix multiplication becomes even more fascinating the bigger these models get! ๐Ÿคฏ๐Ÿ“ˆ QKV for the win! ๐Ÿ†๐Ÿ”‘๐Ÿ“š GitHub: https://github.com/wentasah/mmul-anim Slides: https://cw.fel.cvut.cz/wiki/_media/courses/b4m36esw/esw09_2019.pdf ๐Ÿ“‘๐ŸŽ“
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2024-07-08T18:32:40.000Z
2024-07-08T18:57:40.799Z
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/posts/singhsidhukuldeep/583121742800907
588
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[ { "type": "text", "value": "Introducing the first two projects on the HFforLegal community: the 'Laws' dataset and the associated search tool based on ", "raw": "Introducing the first two projects on the HFforLegal community: the 'Laws' dataset and the associated search tool based on ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@nreimers", "resource": null, "url": null, "href": null, "user": "nreimers", "lang": null, "code": null, "label": null }, { "type": "text", "value": " and ", "raw": " and ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@tomaarsen", "resource": null, "url": null, "href": null, "user": "tomaarsen", "lang": null, "code": null, "label": null }, { "type": "text", "value": "'s Sentence Transformers library ๐Ÿค—", "raw": "'s Sentence Transformers library ๐Ÿค—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The objective of these two tools is to centralize in a single format a set of rules from different countries and legal systems in order to facilitate NLP in the field of comparative law, enabling more accurate and comprehensive legal analysis across different jurisdictions ๐ŸŒ", "raw": "The objective of these two tools is to centralize in a single format a set of rules from different countries and legal systems in order to facilitate NLP in the field of comparative law, enabling more accurate and comprehensive legal analysis across different jurisdictions ๐ŸŒ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Link to the dataset : ", "raw": "Link to the dataset : ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/HFforLegal/laws", "resource": { "type": "dataset", "id": "HFforLegal/laws", "discussionNum": null }, "url": "https://huggingface.co/datasets/HFforLegal/laws", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Link to the space: ", "raw": "Link to the space: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/HFforLegal/laws-retrieval", "resource": { "type": "space", "id": "HFforLegal/laws-retrieval", "discussionNum": null }, "url": "https://huggingface.co/spaces/HFforLegal/laws-retrieval", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We need your contributions to enrich this new knowledge base, and you will find in the 'Laws' dataset all the information you need to format your data and submit them to the appropriate split.", "raw": "We need your contributions to enrich this new knowledge base, and you will find in the 'Laws' dataset all the information you need to format your data and submit them to the appropriate split.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Introducing the first two projects on the HFforLegal community: the 'Laws' dataset and the associated search tool based on @nreimers and @tomaarsen's Sentence Transformers library ๐Ÿค— The objective of these two tools is to centralize in a single format a set of rules from different countries and legal systems in order to facilitate NLP in the field of comparative law, enabling more accurate and comprehensive legal analysis across different jurisdictions ๐ŸŒ Link to the dataset : https://huggingface.co/datasets/HFforLegal/laws Link to the space: https://huggingface.co/spaces/HFforLegal/laws-retrieval We need your contributions to enrich this new knowledge base, and you will find in the 'Laws' dataset all the information you need to format your data and submit them to the appropriate split.
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2024-07-08T18:13:07.000Z
2024-07-08T18:13:07.544Z
[]
/posts/louisbrulenaudet/520896157294536
2,109
0
890020620506496
[ { "type": "text", "value": "New cookbook!", "raw": "New cookbook!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I show to to make agentic RAG using Transformers Agents.", "raw": "I show to to make agentic RAG using Transformers Agents.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Compared to vanilla RAG, agentic RAG can:", "raw": "Compared to vanilla RAG, agentic RAG can:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Reformulate the query", "raw": "โœ… Reformulate the query", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Critique the retrived content to re-retrieve if needed", "raw": "โœ… Critique the retrived content to re-retrieve if needed", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โžก๏ธ Score increase of 8.5%! ๐Ÿ’ช (Llama-3-70B-judge)", "raw": "โžก๏ธ Score increase of 8.5%! ๐Ÿ’ช (Llama-3-70B-judge)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Read it here ๐Ÿ‘‰ ", "raw": "Read it here ๐Ÿ‘‰ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/learn/cookbook/agent_rag", "resource": null, "url": null, "href": "https://huggingface.co/learn/cookbook/agent_rag", "user": null, "lang": null, "code": null, "label": null } ]
New cookbook! I show to to make agentic RAG using Transformers Agents. Compared to vanilla RAG, agentic RAG can: โœ… Reformulate the query โœ… Critique the retrived content to re-retrieve if needed โžก๏ธ Score increase of 8.5%! ๐Ÿ’ช (Llama-3-70B-judge) Read it here ๐Ÿ‘‰ https://huggingface.co/learn/cookbook/agent_rag
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2024-07-08T15:16:16.000Z
2024-07-08T15:16:40.147Z
[]
/posts/m-ric/890020620506496
1,864
0
241823096811696
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Animate a portrait with a driving video. Lots of potential fun here ๐Ÿ˜… https://huggingface.co/spaces/KwaiVGI/LivePortrait
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2024-07-08T14:25:37.000Z
2024-07-08T14:25:37.077Z
[]
/posts/fdaudens/241823096811696
2,162
0
330158692626177
[ { "type": "text", "value": "Kolors with VLM support", "raw": "Kolors with VLM support", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I've built a space for using Kolors image generation model with captioner models and prompt enhancers.", "raw": "I've built a space for using Kolors image generation model with captioner models and prompt enhancers.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": 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"https://huggingface.co/gokaygokay/sd3-long-captioner-v2", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/microsoft/Florence-2-base", "resource": { "type": "model", "id": "microsoft/Florence-2-base", "discussionNum": null }, "url": "https://huggingface.co/microsoft/Florence-2-base", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, 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Kolors with VLM support I've built a space for using Kolors image generation model with captioner models and prompt enhancers. - Space with VLM and Prompt Enhancer https://huggingface.co/spaces/gokaygokay/KolorsPlusPlus - Original Space for model https://huggingface.co/spaces/gokaygokay/Kolors - Captioner VLMs - https://huggingface.co/gokaygokay/sd3-long-captioner-v2 - https://huggingface.co/microsoft/Florence-2-base - Prompt Enhancers - https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long - https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance
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2024-07-08T09:53:00.000Z
2024-07-08T09:55:26.573Z
[]
/posts/gokaygokay/330158692626177
6,182
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333361610370068
[ { "type": "text", "value": "Hey everyone!", "raw": "Hey everyone!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Our team just dropped something cool! ๐ŸŽ‰ We've published a new paper on arxiv diving into the foundation model leaderboards across different platforms. We've analyzed the content, operational workflows, and common issues of these leaderboards. From this, we came up with two new concepts: Leaderboard Operations (LBOps) and leaderboard smells.", "raw": "Our team just dropped something cool! ๐ŸŽ‰ We've published a new paper on arxiv diving into the foundation model leaderboards across different platforms. We've analyzed the content, operational workflows, and common issues of these leaderboards. From this, we came up with two new concepts: Leaderboard Operations (LBOps) and leaderboard smells.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We also put together an awesome list with nearly 300 of the latest leaderboards, development tools, and publishing organizations. You can check it out here: ", "raw": "We also put together an awesome list with nearly 300 of the latest leaderboards, development tools, and publishing organizations. You can check it out here: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/SAILResearch/awesome-foundation-model-leaderboards", "resource": null, "url": null, "href": "https://github.com/SAILResearch/awesome-foundation-model-leaderboards", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "If you find it useful or interesting, give us a follow or drop a comment. We'd love to hear your thoughts and get your support! โœจ", "raw": "If you find it useful or interesting, give us a follow or drop a comment. We'd love to hear your thoughts and get your support! โœจ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Link to the paper: ", "raw": "Link to the paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2407.04065", "resource": null, "url": null, "href": "https://arxiv.org/abs/2407.04065", "user": null, "lang": null, "code": null, "label": null } ]
Hey everyone! Our team just dropped something cool! ๐ŸŽ‰ We've published a new paper on arxiv diving into the foundation model leaderboards across different platforms. We've analyzed the content, operational workflows, and common issues of these leaderboards. From this, we came up with two new concepts: Leaderboard Operations (LBOps) and leaderboard smells. We also put together an awesome list with nearly 300 of the latest leaderboards, development tools, and publishing organizations. You can check it out here: https://github.com/SAILResearch/awesome-foundation-model-leaderboards If you find it useful or interesting, give us a follow or drop a comment. We'd love to hear your thoughts and get your support! โœจ Link to the paper: https://arxiv.org/abs/2407.04065
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2024-07-08T03:21:23.000Z
2024-07-09T04:41:16.291Z
[]
/posts/zhiminy/333361610370068
1,984
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[ { "type": "text", "value": "Excited to announce the release of the community version of our guardrails: WalledGuard-C!", "raw": "Excited to announce the release of the community version of our guardrails: WalledGuard-C!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Feel free to use itโ€”compared to Metaโ€™s guardrails, it offers superior performance, being 4x faster. Most importantly, it's free for nearly any use!", "raw": "Feel free to use itโ€”compared to Metaโ€™s guardrails, it offers superior performance, being 4x faster. Most importantly, it's free for nearly any use!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Link: ", "raw": "Link: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/walledai/walledguard-c", "resource": { "type": "model", "id": "walledai/walledguard-c", "discussionNum": null }, "url": "https://huggingface.co/walledai/walledguard-c", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "#AISafety", "raw": "#AISafety", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Excited to announce the release of the community version of our guardrails: WalledGuard-C! Feel free to use itโ€”compared to Metaโ€™s guardrails, it offers superior performance, being 4x faster. Most importantly, it's free for nearly any use! Link: https://huggingface.co/walledai/walledguard-c #AISafety
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2024-07-07T19:42:31.000Z
2024-07-08T14:16:17.736Z
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/posts/RishabhBhardwaj/993060645926299
2,125
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[ { "type": "text", "value": "Anyone knows how to use SD3 for inpaint task ? :)", "raw": "Anyone knows how to use SD3 for inpaint task ? :)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Anyone knows how to use SD3 for inpaint task ? :)
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2024-07-07T17:37:00.000Z
2024-07-07T17:37:00.826Z
[]
/posts/Kkordik/500754105377818
798
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516018925949105
[ { "type": "text", "value": "BrainGPT - Fun Weekend Project:)", "raw": "BrainGPT - Fun Weekend Project:)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Getting creative with a sci-fi 3D point cloud model of the brain - you prompt the model with questions about AI research frameworks that were deeply inspired by parts of the brain, you get a response with related papers ๐Ÿ˜‚", "raw": "Getting creative with a sci-fi 3D point cloud model of the brain - you prompt the model with questions about AI research frameworks that were deeply inspired by parts of the brain, you get a response with related papers ๐Ÿ˜‚", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
BrainGPT - Fun Weekend Project:) Getting creative with a sci-fi 3D point cloud model of the brain - you prompt the model with questions about AI research frameworks that were deeply inspired by parts of the brain, you get a response with related papers ๐Ÿ˜‚
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2024-07-07T16:12:14.000Z
2024-07-07T16:13:24.460Z
[]
/posts/Jaward/516018925949105
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516186436908312
[ { "type": "text", "value": "Yet Another Whisper Finetune", "raw": "Yet Another Whisper Finetune", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/MohamedRashad/Arabic-Whisper-CodeSwitching-Edition", "resource": { "type": "model", "id": "MohamedRashad/Arabic-Whisper-CodeSwitching-Edition", "discussionNum": null }, "url": "https://huggingface.co/MohamedRashad/Arabic-Whisper-CodeSwitching-Edition", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " is a finetune of OpenAI Whisper Large V2 on ", "raw": " is a finetune of OpenAI Whisper Large V2 on ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/MohamedRashad/arabic-english-code-switching", "resource": { "type": "dataset", "id": "MohamedRashad/arabic-english-code-switching", "discussionNum": null }, "url": "https://huggingface.co/datasets/MohamedRashad/arabic-english-code-switching", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " dataset.", "raw": " dataset.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This new finetune is capable of recognizing english words in arabic speech and transcribing the foreign words as it is.", "raw": "This new finetune is capable of recognizing english words in arabic speech and transcribing the foreign words as it is.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Try it out here:", "raw": "Try it out here:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/MohamedRashad/Arabic-Whisper-CodeSwitching-Edition", "resource": { "type": "space", "id": "MohamedRashad/Arabic-Whisper-CodeSwitching-Edition", "discussionNum": null }, "url": "https://huggingface.co/spaces/MohamedRashad/Arabic-Whisper-CodeSwitching-Edition", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Yet Another Whisper Finetune https://huggingface.co/MohamedRashad/Arabic-Whisper-CodeSwitching-Edition is a finetune of OpenAI Whisper Large V2 on https://huggingface.co/datasets/MohamedRashad/arabic-english-code-switching dataset. This new finetune is capable of recognizing english words in arabic speech and transcribing the foreign words as it is. Try it out here: https://huggingface.co/spaces/MohamedRashad/Arabic-Whisper-CodeSwitching-Edition
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2024-07-07T14:46:02.000Z
2024-07-07T14:46:02.911Z
[]
/posts/MohamedRashad/516186436908312
2,160
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252339803025981
[ { "type": "text", "value": "Introducing: The P-FAF Swarm Encoder! Now that I know you can do it with Swarm algorithms, the sky is the limit, beebee! The hotness AI tools over the past month have been LLM models with self referential feedback. AKA, getting them to do stuff on their own, like create data, transform data, etc. What if I told you that was wholly inefficient to do? What if I could replicate the same things and run it on a calculator? Meet P-FAF Swarm Encoder, your first real world proof that this is beyond viable. Plenty more proof to come!", "raw": "Introducing: The P-FAF Swarm Encoder! Now that I know you can do it with Swarm algorithms, the sky is the limit, beebee! The hotness AI tools over the past month have been LLM models with self referential feedback. AKA, getting them to do stuff on their own, like create data, transform data, etc. What if I told you that was wholly inefficient to do? What if I could replicate the same things and run it on a calculator? Meet P-FAF Swarm Encoder, your first real world proof that this is beyond viable. Plenty more proof to come!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Colab Notebook to try it yourself: ", "raw": "Colab Notebook to try it yourself: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/14CRumDep0SEubEiG3DCOCuGNGeZcsUkO?usp=sharing", "resource": null, "url": null, "href": "https://colab.research.google.com/drive/14CRumDep0SEubEiG3DCOCuGNGeZcsUkO?usp=sharing", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Introducing: The P-FAF Swarm Encoder! Now that I know you can do it with Swarm algorithms, the sky is the limit, beebee! The hotness AI tools over the past month have been LLM models with self referential feedback. AKA, getting them to do stuff on their own, like create data, transform data, etc. What if I told you that was wholly inefficient to do? What if I could replicate the same things and run it on a calculator? Meet P-FAF Swarm Encoder, your first real world proof that this is beyond viable. Plenty more proof to come! Colab Notebook to try it yourself: https://colab.research.google.com/drive/14CRumDep0SEubEiG3DCOCuGNGeZcsUkO?usp=sharing
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2024-07-07T11:59:32.000Z
2024-07-07T14:51:00.923Z
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/posts/TuringsSolutions/252339803025981
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[ { "type": "text", "value": "๐Ÿค— Hi HF community!", "raw": "๐Ÿค— Hi HF community!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿค– A vital question that every developer may have asked themselves in the last three years is: how can we improve AI code generation?", "raw": "๐Ÿค– A vital question that every developer may have asked themselves in the last three years is: how can we improve AI code generation?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ˜‡ In my last Community blog post, I talk about Codium AI's AlphaCodium and how they tried to enhance LLMs coding skills with flow engineering: ", "raw": "๐Ÿ˜‡ In my last Community blog post, I talk about Codium AI's AlphaCodium and how they tried to enhance LLMs coding skills with flow engineering: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/as-cle-bert/repetita-iuvant-how-to-improve-ai-code-generation", "resource": null, "url": null, "href": "https://huggingface.co/blog/as-cle-bert/repetita-iuvant-how-to-improve-ai-code-generation", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ Enjoy!", "raw": "๐Ÿš€ Enjoy!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿค— Hi HF community! ๐Ÿค– A vital question that every developer may have asked themselves in the last three years is: how can we improve AI code generation? ๐Ÿ˜‡ In my last Community blog post, I talk about Codium AI's AlphaCodium and how they tried to enhance LLMs coding skills with flow engineering: https://huggingface.co/blog/as-cle-bert/repetita-iuvant-how-to-improve-ai-code-generation ๐Ÿš€ Enjoy!
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2024-07-07T10:31:02.000Z
2024-07-07T10:31:02.647Z
[]
/posts/as-cle-bert/722167127998366
1,830
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216810836314467
[ { "type": "text", "value": "A new paper titled \"STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis\" shows the benefits of integrating static analysis with LLMs. (", "raw": "A new paper titled \"STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis\" shows the benefits of integrating static analysis with LLMs. (", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2406.10018", "resource": null, "url": null, "href": "https://arxiv.org/abs/2406.10018", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ")", "raw": ")", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Authors evaluate 4 key questions:", "raw": "Authors evaluate 4 key questions:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- How does each static analysis integration strategy perform in LLM-based repository-level code completion?", "raw": "- How does each static analysis integration strategy perform in LLM-based repository-level code completion?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> They found that integrating static analysis in the prompting phase (especially with file-level dependencies) can achieve the substantially larger improvements than other phases.", "raw": "> They found that integrating static analysis in the prompting phase (especially with file-level dependencies) can achieve the substantially larger improvements than other phases.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- How do different combinations of integration strategies affect LLM-based repository-level code completion?", "raw": "- How do different combinations of integration strategies affect LLM-based repository-level code completion?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Languages that are easier to analyze like Java show more improvements compared to dynamic languages like Python.", "raw": "> Languages that are easier to analyze like Java show more improvements compared to dynamic languages like Python.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- How do static analysis integration strategies perform when compared or combined with RAG in LLM-based repository-level code completion?", "raw": "- How do static analysis integration strategies perform when compared or combined with RAG in LLM-based repository-level code completion?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Static analysis and RAG are complementary and boost the overall accuracy.", "raw": "> Static analysis and RAG are complementary and boost the overall accuracy.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- What are the online costs of different integration strategies in LLM-based repository-level code completion?", "raw": "- What are the online costs of different integration strategies in LLM-based repository-level code completion?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Combining prompting-phase static analysis and RAG is the best option for cost-effectiveness.", "raw": "> Combining prompting-phase static analysis and RAG is the best option for cost-effectiveness.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "In my ", "raw": "In my ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@owasp", "resource": null, "url": null, "href": null, "user": "owasp", "lang": null, "code": null, "label": null }, { "type": "text", "value": " App Sec keynote last year, I had described how one can do static analysis augmented generation (SaAG) to boost the accuracy of LLM based patches for vulnerability remediation. (you can see the talk here - ", "raw": " App Sec keynote last year, I had described how one can do static analysis augmented generation (SaAG) to boost the accuracy of LLM based patches for vulnerability remediation. (you can see the talk here - ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.youtube.com/watch?v=Cw4-ZnUNVLs", "resource": null, "url": null, "href": "https://www.youtube.com/watch?v=Cw4-ZnUNVLs", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ")", "raw": ")", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
A new paper titled "STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis" shows the benefits of integrating static analysis with LLMs. (https://arxiv.org/abs/2406.10018) Authors evaluate 4 key questions: - How does each static analysis integration strategy perform in LLM-based repository-level code completion? > They found that integrating static analysis in the prompting phase (especially with file-level dependencies) can achieve the substantially larger improvements than other phases. - How do different combinations of integration strategies affect LLM-based repository-level code completion? > Languages that are easier to analyze like Java show more improvements compared to dynamic languages like Python. - How do static analysis integration strategies perform when compared or combined with RAG in LLM-based repository-level code completion? > Static analysis and RAG are complementary and boost the overall accuracy. - What are the online costs of different integration strategies in LLM-based repository-level code completion? > Combining prompting-phase static analysis and RAG is the best option for cost-effectiveness. In my @owasp App Sec keynote last year, I had described how one can do static analysis augmented generation (SaAG) to boost the accuracy of LLM based patches for vulnerability remediation. (you can see the talk here - https://www.youtube.com/watch?v=Cw4-ZnUNVLs)
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2024-07-07T08:49:46.000Z
2024-07-07T08:49:46.975Z
[]
/posts/codelion/216810836314467
2,445
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283084451634409
[ { "type": "text", "value": "Caffe 2 started it, TensorFlow brought it to the masses but PyTorch perfected it! โœจ", "raw": "Caffe 2 started it, TensorFlow brought it to the masses but PyTorch perfected it! โœจ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Good folks at PyTorch are just wizards ๐Ÿง™โ€โ™‚๏ธ. Along with producing one of the best Deep Learning libraries of all time, they just dropped the \"PyTorch Documentary\" ๐ŸŽฅ", "raw": "Good folks at PyTorch are just wizards ๐Ÿง™โ€โ™‚๏ธ. Along with producing one of the best Deep Learning libraries of all time, they just dropped the \"PyTorch Documentary\" ๐ŸŽฅ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "A must-watch! It covers the beginning to now: ", "raw": "A must-watch! It covers the beginning to now: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Caffe 2 โ˜•", "raw": "- Caffe 2 โ˜•", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Torch (with Lua) ๐Ÿ”ฅ", "raw": "- Torch (with Lua) ๐Ÿ”ฅ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Tensorflow ๐Ÿ”„", "raw": "- Tensorflow ๐Ÿ”„", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- PyTorch ๐Ÿ”ฅ", "raw": "- PyTorch ๐Ÿ”ฅ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Taffe IR (later became ONNX - Open Neural Network Exchange) ๐Ÿ”ง", "raw": "- Taffe IR (later became ONNX - Open Neural Network Exchange) ๐Ÿ”ง", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Full Official PyTorch Documentary: Powering the AI Revolution: ", "raw": "Full Official PyTorch Documentary: Powering the AI Revolution: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://youtu.be/rgP_LBtaUEc", "resource": null, "url": null, "href": "https://youtu.be/rgP_LBtaUEc", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Interesting quote: \"PyTorch does not fight for the fastest performance but the easiest user experience!\" ๐ŸŒŸ", "raw": "Interesting quote: \"PyTorch does not fight for the fastest performance but the easiest user experience!\" ๐ŸŒŸ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "That's what Python ๐Ÿ feels like...", "raw": "That's what Python ๐Ÿ feels like...", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "You got to thank ", "raw": "You got to thank ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@Meta", "resource": null, "url": null, "href": null, "user": "Meta", "lang": null, "code": null, "label": null }, { "type": "text", "value": " for open-sourcing it! ๐Ÿค", "raw": " for open-sourcing it! ๐Ÿค", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Caffe 2 started it, TensorFlow brought it to the masses but PyTorch perfected it! โœจ Good folks at PyTorch are just wizards ๐Ÿง™โ€โ™‚๏ธ. Along with producing one of the best Deep Learning libraries of all time, they just dropped the "PyTorch Documentary" ๐ŸŽฅ A must-watch! It covers the beginning to now: - Caffe 2 โ˜• - Torch (with Lua) ๐Ÿ”ฅ - Tensorflow ๐Ÿ”„ - PyTorch ๐Ÿ”ฅ - Taffe IR (later became ONNX - Open Neural Network Exchange) ๐Ÿ”ง Full Official PyTorch Documentary: Powering the AI Revolution: https://youtu.be/rgP_LBtaUEc Interesting quote: "PyTorch does not fight for the fastest performance but the easiest user experience!" ๐ŸŒŸ That's what Python ๐Ÿ feels like... You got to thank @Meta for open-sourcing it! ๐Ÿค
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2024-07-07T06:47:16.000Z
2024-07-07T06:47:16.097Z
[]
/posts/singhsidhukuldeep/283084451634409
574
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536703122300219
[ { "type": "text", "value": "๐Ÿง  How to create more diverse, realistic synthetic AI training data? ", "raw": "๐Ÿง  How to create more diverse, realistic synthetic AI training data? ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@TencentAIGC-Lab", "resource": null, "url": null, "href": null, "user": "TencentAIGC-Lab", "lang": null, "code": null, "label": null }, { "type": "text", "value": " AI Lab created ", "raw": " AI Lab created ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@proj-persona", "resource": null, "url": null, "href": null, "user": "proj-persona", "lang": null, "code": null, "label": null }, { "type": "text", "value": ", a vast collection of 1 billion diverse personas, to help create synthetic data with LLMs that encapsulate a wide array of perspectives, knowledge, experiences, interests, and professions.", "raw": ", a vast collection of 1 billion diverse personas, to help create synthetic data with LLMs that encapsulate a wide array of perspectives, knowledge, experiences, interests, and professions.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "These personas were created with automatically curated data, representing approximately 13% of the worldโ€™s total population.", "raw": "These personas were created with automatically curated data, representing approximately 13% of the worldโ€™s total population.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’ก The authors argue that integrating a persona into data synthesis prompts effectively steers LLMs to adopt specific perspectives, creating unique and relevant synthetic data with minimal effort.", "raw": "๐Ÿ’ก The authors argue that integrating a persona into data synthesis prompts effectively steers LLMs to adopt specific perspectives, creating unique and relevant synthetic data with minimal effort.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "They showcased various practical applications of Persona Hub to demonstrate its effectiveness and versatility in various synthetic data creation scenarios: mathematical and logical reasoning problems, simulating diverse user requests and prompts for LLMs, generating informative and detailed text content across various topics, and more.", "raw": "They showcased various practical applications of Persona Hub to demonstrate its effectiveness and versatility in various synthetic data creation scenarios: mathematical and logical reasoning problems, simulating diverse user requests and prompts for LLMs, generating informative and detailed text content across various topics, and more.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ It's one of the trending datasets on Hugging Face. Digging into it is quite fun! I found one that reminds me of several people I know: \"A journalist who covers technology and innovation in the print and digital media industries.\" It helped generate the prompt attached to this post (about which I'd be curious to know your answers ๐Ÿ˜‰).", "raw": "๐Ÿš€ It's one of the trending datasets on Hugging Face. Digging into it is quite fun! I found one that reminds me of several people I know: \"A journalist who covers technology and innovation in the print and digital media industries.\" It helped generate the prompt attached to this post (about which I'd be curious to know your answers ๐Ÿ˜‰).", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Synthetic data is a hot topic in AI. It will be interesting to see if this research could help make LLMs more robust, versatile, and capable of handling a wide array of real-world scenarios.", "raw": "Synthetic data is a hot topic in AI. It will be interesting to see if this research could help make LLMs more robust, versatile, and capable of handling a wide array of real-world scenarios.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰Explore the dataset: ", "raw": "๐Ÿ‘‰Explore the dataset: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/proj-persona/PersonaHub", "resource": { "type": "dataset", "id": "proj-persona/PersonaHub", "discussionNum": null }, "url": "https://huggingface.co/datasets/proj-persona/PersonaHub", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰ Read the paper: ", "raw": "๐Ÿ‘‰ Read the paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/pdf/2406.20094", "resource": null, "url": null, "href": "https://arxiv.org/pdf/2406.20094", "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿง  How to create more diverse, realistic synthetic AI training data? @TencentAIGC-Lab AI Lab created @proj-persona, a vast collection of 1 billion diverse personas, to help create synthetic data with LLMs that encapsulate a wide array of perspectives, knowledge, experiences, interests, and professions. These personas were created with automatically curated data, representing approximately 13% of the worldโ€™s total population. ๐Ÿ’ก The authors argue that integrating a persona into data synthesis prompts effectively steers LLMs to adopt specific perspectives, creating unique and relevant synthetic data with minimal effort. They showcased various practical applications of Persona Hub to demonstrate its effectiveness and versatility in various synthetic data creation scenarios: mathematical and logical reasoning problems, simulating diverse user requests and prompts for LLMs, generating informative and detailed text content across various topics, and more. ๐Ÿš€ It's one of the trending datasets on Hugging Face. Digging into it is quite fun! I found one that reminds me of several people I know: "A journalist who covers technology and innovation in the print and digital media industries." It helped generate the prompt attached to this post (about which I'd be curious to know your answers ๐Ÿ˜‰). Synthetic data is a hot topic in AI. It will be interesting to see if this research could help make LLMs more robust, versatile, and capable of handling a wide array of real-world scenarios. ๐Ÿ‘‰Explore the dataset: https://huggingface.co/datasets/proj-persona/PersonaHub ๐Ÿ‘‰ Read the paper: https://arxiv.org/pdf/2406.20094
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2024-07-07T01:44:01.000Z
2024-07-07T01:44:01.462Z
[]
/posts/fdaudens/536703122300219
3,348
0
131317141629549
[ { "type": "text", "value": "I updated ", "raw": "I updated ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/nroggendorff/llava", "resource": { "type": "space", "id": "nroggendorff/llava", "discussionNum": null }, "url": "https://huggingface.co/spaces/nroggendorff/llava", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " to be even worse. The point of this space is to point out how bad the base model for Llava is, and how without an image it struggles quite a bit. In the new update, from my testing, even if you ask the model about the image, it won't be able to tell you. I experimented with quite a few things, including an image where all the values are 0 (shoutout np.zeros), an image with the most generic portrait photo I could think of (black hair, brown eyes, plain white shirt, etc..) (generated with ", "raw": " to be even worse. The point of this space is to point out how bad the base model for Llava is, and how without an image it struggles quite a bit. In the new update, from my testing, even if you ask the model about the image, it won't be able to tell you. I experimented with quite a few things, including an image where all the values are 0 (shoutout np.zeros), an image with the most generic portrait photo I could think of (black hair, brown eyes, plain white shirt, etc..) (generated with ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/nroggendorff/epicrealismxl", "resource": { "type": "space", "id": "nroggendorff/epicrealismxl", "discussionNum": null }, "url": "https://huggingface.co/spaces/nroggendorff/epicrealismxl", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "), an image that has text that reads \"The image you are looking for is unavailable\", and much.. much more.. *sigh*. The image I'm ending up with seems to work best. I don't think it would work with other multi-modal LLMs, but I'm not sure. If you find a problem, feel free to message me on the [huggingface discord](", "raw": "), an image that has text that reads \"The image you are looking for is unavailable\", and much.. much more.. *sigh*. The image I'm ending up with seems to work best. I don't think it would work with other multi-modal LLMs, but I'm not sure. If you find a problem, feel free to message me on the [huggingface discord](", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "http://hf.co/join/discord", "resource": null, "url": null, "href": "http://hf.co/join/discord", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "), or open a pull request.", "raw": "), or open a pull request.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
I updated https://huggingface.co/spaces/nroggendorff/llava to be even worse. The point of this space is to point out how bad the base model for Llava is, and how without an image it struggles quite a bit. In the new update, from my testing, even if you ask the model about the image, it won't be able to tell you. I experimented with quite a few things, including an image where all the values are 0 (shoutout np.zeros), an image with the most generic portrait photo I could think of (black hair, brown eyes, plain white shirt, etc..) (generated with https://huggingface.co/spaces/nroggendorff/epicrealismxl), an image that has text that reads "The image you are looking for is unavailable", and much.. much more.. *sigh*. The image I'm ending up with seems to work best. I don't think it would work with other multi-modal LLMs, but I'm not sure. If you find a problem, feel free to message me on the [huggingface discord](http://hf.co/join/discord), or open a pull request.
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[]
[]
[]
2024-07-07T00:34:16.000Z
2024-07-16T03:34:24.684Z
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/posts/nroggendorff/131317141629549
585
2
373480630588243
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๐Ÿ“ข Delighted to share the most recent and valuable contributions to the book-related NLP domain ๐Ÿ’Ž To push forward deeper understanding of the characters ๐Ÿ‘จโ€๐Ÿ‘ฉโ€๐Ÿ‘งโ€๐Ÿ‘ฆ from literature novel books itself ๐Ÿ“– by machine learning models ๐Ÿค–, releasing the most-accessible version v1.0 of the related workflow, adopted for ParlAI ๐Ÿฆœ agents ๐ŸŒŸ https://github.com/nicolay-r/book-persona-retriever/tree/v1.0 Feel free to follow / share / comment in order to advance the related direction!
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2024-07-06T22:58:40.000Z
2024-07-07T07:43:31.250Z
[]
/posts/nicolay-r/373480630588243
496
0
650895883261134
[ { "type": "text", "value": "AIโ€™s Cognitive Mirror: The Illusion of Consciousness in the Digital Age", "raw": "AIโ€™s Cognitive Mirror: The Illusion of Consciousness in the Digital Age", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://empereur-pirate.medium.com/ais-cognitive-mirror-the-illusion-of-consciousness-in-the-digital-age-46f3ddae60a6", "resource": null, "url": null, "href": "https://empereur-pirate.medium.com/ais-cognitive-mirror-the-illusion-of-consciousness-in-the-digital-age-46f3ddae60a6", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This text explores the philosophical implications of AI's potential for spiritual consciousness. It argues that AI, while capable of complex thought processes, lacks true self-awareness due to the absence of sensory perception. The article discusses how human consciousness develops through sensory experiences, contrasting this with AI's purely computational nature. It examines AI as a mirror of human thought, capable of impressive language processing and knowledge synthesis, yet different from human psyche. The text also touches on the spiritual and ethical dimensions of AI development, drawing parallels with historical technological revolutions and religious symbolism. It concludes by considering AI's potential role in decision-making processes and its impact on various fields, while emphasizing the need for ethical safeguards and critical evaluation of AI-generated content.", "raw": "This text explores the philosophical implications of AI's potential for spiritual consciousness. It argues that AI, while capable of complex thought processes, lacks true self-awareness due to the absence of sensory perception. The article discusses how human consciousness develops through sensory experiences, contrasting this with AI's purely computational nature. It examines AI as a mirror of human thought, capable of impressive language processing and knowledge synthesis, yet different from human psyche. The text also touches on the spiritual and ethical dimensions of AI development, drawing parallels with historical technological revolutions and religious symbolism. It concludes by considering AI's potential role in decision-making processes and its impact on various fields, while emphasizing the need for ethical safeguards and critical evaluation of AI-generated content.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
AIโ€™s Cognitive Mirror: The Illusion of Consciousness in the Digital Age https://empereur-pirate.medium.com/ais-cognitive-mirror-the-illusion-of-consciousness-in-the-digital-age-46f3ddae60a6 This text explores the philosophical implications of AI's potential for spiritual consciousness. It argues that AI, while capable of complex thought processes, lacks true self-awareness due to the absence of sensory perception. The article discusses how human consciousness develops through sensory experiences, contrasting this with AI's purely computational nature. It examines AI as a mirror of human thought, capable of impressive language processing and knowledge synthesis, yet different from human psyche. The text also touches on the spiritual and ethical dimensions of AI development, drawing parallels with historical technological revolutions and religious symbolism. It concludes by considering AI's potential role in decision-making processes and its impact on various fields, while emphasizing the need for ethical safeguards and critical evaluation of AI-generated content.
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2024-07-06T19:51:46.000Z
2024-07-09T15:07:41.037Z
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/posts/Empereur-Pirate/650895883261134
2,039
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286266640733072
[ { "type": "text", "value": "Just released the GitVerse Code Dataset - ", "raw": "Just released the GitVerse Code Dataset - ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/nyuuzyou/gitverse-code", "resource": { "type": "dataset", "id": "nyuuzyou/gitverse-code", "discussionNum": null }, "url": "https://huggingface.co/datasets/nyuuzyou/gitverse-code", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“Š Dataset highlights:", "raw": "๐Ÿ“Š Dataset highlights:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 30 GB of unique code extracted from over 400 GB of analyzed data", "raw": "- 30 GB of unique code extracted from over 400 GB of analyzed data", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 9,014 repositories", "raw": "- 9,014 repositories", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 2,804,216 unique code files", "raw": "- 2,804,216 unique code files", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 419 different file types", "raw": "- 419 different file types", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Multilingual: various programming languages", "raw": "- Multilingual: various programming languages", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒ Sourced from GitVerse, a Russian GitHub alternative opened in 2024.", "raw": "๐ŸŒ Sourced from GitVerse, a Russian GitHub alternative opened in 2024.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Let me know your thoughts.", "raw": "Let me know your thoughts.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Just released the GitVerse Code Dataset - https://huggingface.co/datasets/nyuuzyou/gitverse-code. ๐Ÿ“Š Dataset highlights: - 30 GB of unique code extracted from over 400 GB of analyzed data - 9,014 repositories - 2,804,216 unique code files - 419 different file types - Multilingual: various programming languages ๐ŸŒ Sourced from GitVerse, a Russian GitHub alternative opened in 2024. Let me know your thoughts.
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2024-07-06T17:25:38.000Z
2024-07-06T17:25:38.217Z
[]
/posts/nyuuzyou/286266640733072
1,113
0
912220276203725
[ { "type": "text", "value": "Kolors: Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis", "raw": "Kolors: Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Kolors is a large-scale text-to-image generation model based on latent diffusion, developed by the Kuaishou Kolors team.", "raw": "Kolors is a large-scale text-to-image generation model based on latent diffusion, developed by the Kuaishou Kolors team.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Hugging Face Spaces", "raw": "Hugging Face Spaces", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/gokaygokay/Kolors", "resource": { "type": "space", "id": "gokaygokay/Kolors", "discussionNum": null }, "url": "https://huggingface.co/spaces/gokaygokay/Kolors", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Model Page", "raw": "Model Page", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Kwai-Kolors/Kolors", "resource": { "type": "model", "id": "Kwai-Kolors/Kolors", "discussionNum": null }, "url": "https://huggingface.co/Kwai-Kolors/Kolors", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Kolors: Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis Kolors is a large-scale text-to-image generation model based on latent diffusion, developed by the Kuaishou Kolors team. Hugging Face Spaces - https://huggingface.co/spaces/gokaygokay/Kolors Model Page - https://huggingface.co/Kwai-Kolors/Kolors
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2024-07-06T16:27:16.000Z
2024-07-06T16:27:16.903Z
[]
/posts/gokaygokay/912220276203725
5,021
0
852072398540203
[ { "type": "text", "value": "Really got amazing results with our InstantID next level. Tutorial will be published soon hopefully. This is the best ever 0-shot likeliness and high quality I have ever got.", "raw": "Really got amazing results with our InstantID next level. Tutorial will be published soon hopefully. This is the best ever 0-shot likeliness and high quality I have ever got.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Really got amazing results with our InstantID next level. Tutorial will be published soon hopefully. This is the best ever 0-shot likeliness and high quality I have ever got.
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2024-07-06T01:39:05.000Z
2024-07-06T01:39:05.486Z
[]
/posts/MonsterMMORPG/852072398540203
2,417
0
258139874670492
[ { "type": "text", "value": "Remember the recently released GLM-4 from Tsinghua University ๐ŸŽ‰", "raw": "Remember the recently released GLM-4 from Tsinghua University ๐ŸŽ‰", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Now we have an open-source version of it continuously trained for multilingual code generation! ๐ŸŒ", "raw": "Now we have an open-source version of it continuously trained for multilingual code generation! ๐ŸŒ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It beats CodeLlama 70B (almost 7x size) and is competitive with DeepSeek Coder 33B and Qwen 2 ๐Ÿ’ช", "raw": "It beats CodeLlama 70B (almost 7x size) and is competitive with DeepSeek Coder 33B and Qwen 2 ๐Ÿ’ช", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Just like almost every other coding model, it has a 128K context ๐Ÿ“œ", "raw": "Just like almost every other coding model, it has a 128K context ๐Ÿ“œ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Supports:", "raw": "Supports:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Code completion ๐Ÿ–‹๏ธ", "raw": "- Code completion ๐Ÿ–‹๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Code generation ๐Ÿ› ๏ธ", "raw": "- Code generation ๐Ÿ› ๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Code interpreter ๐Ÿ’ก", "raw": "- Code interpreter ๐Ÿ’ก", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Web search ๐Ÿ”", "raw": "- Web search ๐Ÿ”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Function call ๐Ÿ“ž", "raw": "- Function call ๐Ÿ“ž", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Repository-level code Q&A ๐Ÿ—‚๏ธ", "raw": "- Repository-level code Q&A ๐Ÿ—‚๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Benchmarks 48.9 and 40.4 for the complete and instruct tasks of BigCodeBench ๐Ÿ“Š", "raw": "Benchmarks 48.9 and 40.4 for the complete and instruct tasks of BigCodeBench ๐Ÿ“Š", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It still falls behind DeepSeek-Coder-V2. While this might have fewer parameters, DSC V2 is a MoE model with only ~2B active parameters ๐Ÿค”", "raw": "It still falls behind DeepSeek-Coder-V2. While this might have fewer parameters, DSC V2 is a MoE model with only ~2B active parameters ๐Ÿค”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Good to see more efficient coding LLMs, but DeepSeek-Coder-V2 is just too good ๐Ÿ†", "raw": "Good to see more efficient coding LLMs, but DeepSeek-Coder-V2 is just too good ๐Ÿ†", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Codel: ", "raw": "Codel: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/THUDM/CodeGeeX4", "resource": null, "url": null, "href": "https://github.com/THUDM/CodeGeeX4", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Model weights: ", "raw": "Model weights: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/THUDM/codegeex4-all-9b", "resource": { "type": "model", "id": "THUDM/codegeex4-all-9b", "discussionNum": null }, "url": "https://huggingface.co/THUDM/codegeex4-all-9b", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Remember the recently released GLM-4 from Tsinghua University ๐ŸŽ‰ Now we have an open-source version of it continuously trained for multilingual code generation! ๐ŸŒ It beats CodeLlama 70B (almost 7x size) and is competitive with DeepSeek Coder 33B and Qwen 2 ๐Ÿ’ช Just like almost every other coding model, it has a 128K context ๐Ÿ“œ Supports: - Code completion ๐Ÿ–‹๏ธ - Code generation ๐Ÿ› ๏ธ - Code interpreter ๐Ÿ’ก - Web search ๐Ÿ” - Function call ๐Ÿ“ž - Repository-level code Q&A ๐Ÿ—‚๏ธ Benchmarks 48.9 and 40.4 for the complete and instruct tasks of BigCodeBench ๐Ÿ“Š It still falls behind DeepSeek-Coder-V2. While this might have fewer parameters, DSC V2 is a MoE model with only ~2B active parameters ๐Ÿค” Good to see more efficient coding LLMs, but DeepSeek-Coder-V2 is just too good ๐Ÿ† Codel: https://github.com/THUDM/CodeGeeX4 Model weights: https://huggingface.co/THUDM/codegeex4-all-9b
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2024-07-05T19:19:36.000Z
2024-07-06T04:32:26.107Z
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[ { "type": "text", "value": "I believe in order to make models reach Human-Level Learning, serious students can start by developing an intelligent neuromorphic agent. We develop an intelligent agent and make it learn about grammar patterns as well as about different word categories through symbolic representations, following which we dwell into making the agent learn about other rules of the Language.", "raw": "I believe in order to make models reach Human-Level Learning, serious students can start by developing an intelligent neuromorphic agent. We develop an intelligent agent and make it learn about grammar patterns as well as about different word categories through symbolic representations, following which we dwell into making the agent learn about other rules of the Language.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "In parallel with grammar learning, the agent would also use language grounding techniques to link words to their sensory representations and abstract concepts which would mean the agent learns about the word meanings, synonyms, antonyms, and semantic relationships from both textual data as well as perceptual experiences. ", "raw": "In parallel with grammar learning, the agent would also use language grounding techniques to link words to their sensory representations and abstract concepts which would mean the agent learns about the word meanings, synonyms, antonyms, and semantic relationships from both textual data as well as perceptual experiences. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The result would be the agent developing a rich lexicon and conceptual knowledge base that underlies its language understanding as well as generation. With this basic knowledge of grammar and word meanings, the agent can then learn to synthesize words and phrases so as to express specific ideas or concepts. Building on this, the agent would then learn how to generate complete sentences which the agent would continuously refine and improve. Eventually the agent would learn how to generate sequence of sentences in the form of dialogues or narratives, taking into account context, goals, as well as user-feedback. ", "raw": "The result would be the agent developing a rich lexicon and conceptual knowledge base that underlies its language understanding as well as generation. With this basic knowledge of grammar and word meanings, the agent can then learn to synthesize words and phrases so as to express specific ideas or concepts. Building on this, the agent would then learn how to generate complete sentences which the agent would continuously refine and improve. Eventually the agent would learn how to generate sequence of sentences in the form of dialogues or narratives, taking into account context, goals, as well as user-feedback. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I believe that by gradually learning how to improve their responses, the agent would gradually also acquire the ability to generate coherent, meaningful, and contextually appropriate language. This would allow them to reason without hallucinating which LLMs struggle at. ", "raw": "I believe that by gradually learning how to improve their responses, the agent would gradually also acquire the ability to generate coherent, meaningful, and contextually appropriate language. This would allow them to reason without hallucinating which LLMs struggle at. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Developing such agents would not require a lot of compute and the code would be simple & easy to understand. It will definitely introduce everyone to symbolic AI and making agents which are good at reasoning tasks. Thus solving a crucial problem with LLMs. We have used a similar architecture to make our model learn constantly. Do sign up as we start opening access next week at ", "raw": "Developing such agents would not require a lot of compute and the code would be simple & easy to understand. It will definitely introduce everyone to symbolic AI and making agents which are good at reasoning tasks. Thus solving a crucial problem with LLMs. We have used a similar architecture to make our model learn constantly. Do sign up as we start opening access next week at ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://octave-x.com/", "resource": null, "url": null, "href": "https://octave-x.com/", "user": null, "lang": null, "code": null, "label": null } ]
I believe in order to make models reach Human-Level Learning, serious students can start by developing an intelligent neuromorphic agent. We develop an intelligent agent and make it learn about grammar patterns as well as about different word categories through symbolic representations, following which we dwell into making the agent learn about other rules of the Language. In parallel with grammar learning, the agent would also use language grounding techniques to link words to their sensory representations and abstract concepts which would mean the agent learns about the word meanings, synonyms, antonyms, and semantic relationships from both textual data as well as perceptual experiences. The result would be the agent developing a rich lexicon and conceptual knowledge base that underlies its language understanding as well as generation. With this basic knowledge of grammar and word meanings, the agent can then learn to synthesize words and phrases so as to express specific ideas or concepts. Building on this, the agent would then learn how to generate complete sentences which the agent would continuously refine and improve. Eventually the agent would learn how to generate sequence of sentences in the form of dialogues or narratives, taking into account context, goals, as well as user-feedback. I believe that by gradually learning how to improve their responses, the agent would gradually also acquire the ability to generate coherent, meaningful, and contextually appropriate language. This would allow them to reason without hallucinating which LLMs struggle at. Developing such agents would not require a lot of compute and the code would be simple & easy to understand. It will definitely introduce everyone to symbolic AI and making agents which are good at reasoning tasks. Thus solving a crucial problem with LLMs. We have used a similar architecture to make our model learn constantly. Do sign up as we start opening access next week at https://octave-x.com/
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2024-07-05T15:31:16.000Z
2024-09-14T09:07:05.053Z
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/posts/Tar9897/162229581303235
3,435
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[ { "type": "text", "value": "ColPali: A new approach to efficient and intelligent document retrieval ๐Ÿš€", "raw": "ColPali: A new approach to efficient and intelligent document retrieval ๐Ÿš€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Our latest research paper, \"ColPali: Efficient Document Retrieval with Vision Language Models,\" introduces a groundbreaking approach to large-scale visual document analysis. By leveraging Vision Language Models (VLMs), we have created a new framework for document retrieval that's both powerful and efficient.", "raw": "Our latest research paper, \"ColPali: Efficient Document Retrieval with Vision Language Models,\" introduces a groundbreaking approach to large-scale visual document analysis. By leveraging Vision Language Models (VLMs), we have created a new framework for document retrieval that's both powerful and efficient.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Key Insights:", "raw": "Key Insights:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’ก ColPali combines ColBERT's multi-vector strategy with VLMs' document understanding capabilities", "raw": "๐Ÿ’ก ColPali combines ColBERT's multi-vector strategy with VLMs' document understanding capabilities", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โš™๏ธ ColPali is based on PaliGemma-3B (SigLIP, Gemma-2B) + a linear projection layer and is trained to maximize the similarity between the document and the query embeddings", "raw": "โš™๏ธ ColPali is based on PaliGemma-3B (SigLIP, Gemma-2B) + a linear projection layer and is trained to maximize the similarity between the document and the query embeddings", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“Š The Vision Document Retrieval benchmark (ViDoRe) is a challenging dataset that spans various industry topics and aims at matching real-life retrieval scenarios", "raw": "๐Ÿ“Š The Vision Document Retrieval benchmark (ViDoRe) is a challenging dataset that spans various industry topics and aims at matching real-life retrieval scenarios", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ† ColPali outperforms existing models on all datasets in ViDoRe (average NDCG@5 of 81.3% vs 67.0% for the best baseline model)", "raw": "๐Ÿ† ColPali outperforms existing models on all datasets in ViDoRe (average NDCG@5 of 81.3% vs 67.0% for the best baseline model)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โšก ColPali is faster at document embedding compared to traditional PDF parser pipelines, making ColPali viable for industrial use", "raw": "โšก ColPali is faster at document embedding compared to traditional PDF parser pipelines, making ColPali viable for industrial use", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ” ColPali is highly interpretable thanks to patch-based similarity maps", "raw": "๐Ÿ” ColPali is highly interpretable thanks to patch-based similarity maps", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Dive deeper into ColPali and explore our resources:", "raw": "Dive deeper into ColPali and explore our resources:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“‘ Full paper: arxiv.org/abs/2407.01449", "raw": "๐Ÿ“‘ Full paper: arxiv.org/abs/2407.01449", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ› ๏ธ Datasets, model weights, evaluation code, leaderboard, demos: huggingface.co/vidore", "raw": "๐Ÿ› ๏ธ Datasets, model weights, evaluation code, leaderboard, demos: huggingface.co/vidore", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Shoutout to my amazing co-authors Manuel Faysse (", "raw": "Shoutout to my amazing co-authors Manuel Faysse (", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@manu", "resource": null, "url": null, "href": null, "user": "manu", "lang": null, "code": null, "label": null }, { "type": "text", "value": ") and Hugues Sibille (", "raw": ") and Hugues Sibille (", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@HugSib", "resource": null, "url": null, "href": null, "user": "HugSib", "lang": null, "code": null, "label": null }, { "type": "text", "value": "). We are grateful for the invaluable feedback from Bilel Omrani, Gautier Viaud, Celine Hudelot, and Pierre Colombo. This work is sponsored by ILLUIN Technology. โœจ ", "raw": "). We are grateful for the invaluable feedback from Bilel Omrani, Gautier Viaud, Celine Hudelot, and Pierre Colombo. This work is sponsored by ILLUIN Technology. โœจ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
ColPali: A new approach to efficient and intelligent document retrieval ๐Ÿš€ Our latest research paper, "ColPali: Efficient Document Retrieval with Vision Language Models," introduces a groundbreaking approach to large-scale visual document analysis. By leveraging Vision Language Models (VLMs), we have created a new framework for document retrieval that's both powerful and efficient. Key Insights: ๐Ÿ’ก ColPali combines ColBERT's multi-vector strategy with VLMs' document understanding capabilities โš™๏ธ ColPali is based on PaliGemma-3B (SigLIP, Gemma-2B) + a linear projection layer and is trained to maximize the similarity between the document and the query embeddings ๐Ÿ“Š The Vision Document Retrieval benchmark (ViDoRe) is a challenging dataset that spans various industry topics and aims at matching real-life retrieval scenarios ๐Ÿ† ColPali outperforms existing models on all datasets in ViDoRe (average NDCG@5 of 81.3% vs 67.0% for the best baseline model) โšก ColPali is faster at document embedding compared to traditional PDF parser pipelines, making ColPali viable for industrial use ๐Ÿ” ColPali is highly interpretable thanks to patch-based similarity maps Dive deeper into ColPali and explore our resources: ๐Ÿ“‘ Full paper: arxiv.org/abs/2407.01449 ๐Ÿ› ๏ธ Datasets, model weights, evaluation code, leaderboard, demos: huggingface.co/vidore Shoutout to my amazing co-authors Manuel Faysse (@manu) and Hugues Sibille (@HugSib). We are grateful for the invaluable feedback from Bilel Omrani, Gautier Viaud, Celine Hudelot, and Pierre Colombo. This work is sponsored by ILLUIN Technology. โœจ
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2024-07-05T13:56:07.000Z
2024-07-05T13:56:07.429Z
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[ { "type": "text", "value": "๐Ÿš€๐Ÿ•บ๐ŸŒŸ New Research Alert (Avatars Collection)! ๐ŸŒŸ๐Ÿ’ƒ๐Ÿš€", "raw": "๐Ÿš€๐Ÿ•บ๐ŸŒŸ New Research Alert (Avatars Collection)! ๐ŸŒŸ๐Ÿ’ƒ๐Ÿš€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“„ Title: Expressive Gaussian Human Avatars from Monocular RGB Video ๐Ÿ”", "raw": "๐Ÿ“„ Title: Expressive Gaussian Human Avatars from Monocular RGB Video ๐Ÿ”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ Description: The new EVA model enhances the expressiveness of digital avatars by using 3D Gaussians and SMPL-X to capture fine-grained hand and face details from monocular RGB video.", "raw": "๐Ÿ“ Description: The new EVA model enhances the expressiveness of digital avatars by using 3D Gaussians and SMPL-X to capture fine-grained hand and face details from monocular RGB video.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘ฅ Authors: Hezhen Hu, Zhiwen Fan, Tianhao Wu, Yihan Xi, Seoyoung Lee, Georgios Pavlakos, and Zhangyang 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Gaussian Human Avatars from Monocular RGB Video (2407.03204)" }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒ Github Page: ", "raw": "๐ŸŒ Github Page: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://evahuman.github.io/", "resource": null, "url": null, "href": "https://evahuman.github.io/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ Repository: ", "raw": "๐Ÿ“ Repository: ", "resource": 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"\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ ICCV-2023-Papers: ", "raw": "๐Ÿš€ ICCV-2023-Papers: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/DmitryRyumin/ICCV-2023-Papers", "resource": null, "url": null, "href": "https://github.com/DmitryRyumin/ICCV-2023-Papers", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "raw": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "resource": { "type": "space", "id": "DmitryRyumin/NewEraAI-Papers", "discussionNum": null }, "url": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " curated by ", "raw": " curated by ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@DmitryRyumin", "resource": null, "url": null, "href": null, "user": "DmitryRyumin", "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ Added to the Avatars Collection: ", "raw": "๐Ÿš€ Added to the Avatars Collection: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "resource": { "type": "collection", "id": "DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "discussionNum": null }, "url": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ” Keywords: #DigitalAvatars #3DModeling #ComputerVision #MonocularVideo #SMPLX #3DGaussians #AvatarExpressiveness #HandTracking #FacialExpressions #AI #MachineLearning", "raw": "๐Ÿ” Keywords: #DigitalAvatars #3DModeling #ComputerVision #MonocularVideo #SMPLX #3DGaussians #AvatarExpressiveness #HandTracking #FacialExpressions #AI #MachineLearning", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿš€๐Ÿ•บ๐ŸŒŸ New Research Alert (Avatars Collection)! ๐ŸŒŸ๐Ÿ’ƒ๐Ÿš€ ๐Ÿ“„ Title: Expressive Gaussian Human Avatars from Monocular RGB Video ๐Ÿ” ๐Ÿ“ Description: The new EVA model enhances the expressiveness of digital avatars by using 3D Gaussians and SMPL-X to capture fine-grained hand and face details from monocular RGB video. ๐Ÿ‘ฅ Authors: Hezhen Hu, Zhiwen Fan, Tianhao Wu, Yihan Xi, Seoyoung Lee, Georgios Pavlakos, and Zhangyang Wang ๐Ÿ“„ Paper: https://huggingface.co/papers/2407.03204 ๐ŸŒ Github Page: https://evahuman.github.io/ ๐Ÿ“ Repository: https://github.com/evahuman/EVA ๐Ÿš€ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers ๐Ÿš€ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers ๐Ÿš€ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers ๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin ๐Ÿš€ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36 ๐Ÿ” Keywords: #DigitalAvatars #3DModeling #ComputerVision #MonocularVideo #SMPLX #3DGaussians #AvatarExpressiveness #HandTracking #FacialExpressions #AI #MachineLearning
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2024-07-05T09:34:42.000Z
2024-07-05T09:34:42.628Z
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Weclome to use MInference, which leverages the dynamic sparse nature of LLMs' attention, which exhibits some static patterns, to speed up the pre-filling for million tokens LLMs. It first determines offline which sparse pattern each head belongs to, then approximates the sparse index online and dynamically computes attention with the optimal custom kernels. This approach achieves up to a 10x speedup for pre-filling on an A100 while maintaining accuracy with 1M tokens. For more detail please check, project page: https://aka.ms/MInference code: https://github.com/microsoft/MInference paper: https://huggingface.co/papers/2407.02490 hf demo: https://huggingface.co/spaces/microsoft/MInference
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2024-07-04T17:53:43.000Z
2024-07-04T17:54:47.262Z
[]
/posts/iofu728/748763977309586
1,067
0
328881196495518
[ { "type": "text", "value": "New dataset filtering feature just dropped! ๐Ÿค—๐Ÿš€", "raw": "New dataset filtering feature just dropped! ๐Ÿค—๐Ÿš€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Find exactly what you need with filters for:", "raw": "Find exactly what you need with filters for:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Modalities (text, image, audio, etc.)", "raw": "- Modalities (text, image, audio, etc.)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Dataset size", "raw": "- Dataset size", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- File format", "raw": "- File format", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Try it now: ", "raw": "Try it now: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/datasets", "resource": null, "url": null, "href": "https://huggingface.co/datasets", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "What other filters would you find useful? Drop your ideas!", "raw": "What other filters would you find useful? Drop your ideas!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
New dataset filtering feature just dropped! ๐Ÿค—๐Ÿš€ Find exactly what you need with filters for: - Modalities (text, image, audio, etc.) - Dataset size - File format Try it now: https://huggingface.co/datasets What other filters would you find useful? Drop your ideas!
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2024-07-04T16:07:28.000Z
2024-07-04T16:07:28.663Z
[]
/posts/fdaudens/328881196495518
989
0
936208234528035
[ { "type": "text", "value": "Hi ", "raw": "Hi ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@jonoirwin", "resource": null, "url": null, "href": null, "user": "jonoirwin", "lang": null, "code": null, "label": null }, { "type": "text", "value": "! Big fan of ", "raw": "! Big fan of ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://fastvoiceagent.cerebrium.ai/", "resource": null, "url": null, "href": "https://fastvoiceagent.cerebrium.ai/", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ๐Ÿ”ฅ", "raw": " ๐Ÿ”ฅ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I'd be super happy to give you a GPU grant to host it on a Space, it would allow more people to discover and use it!", "raw": "I'd be super happy to give you a GPU grant to host it on a Space, it would allow more people to discover and use it!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Hi @jonoirwin! Big fan of https://fastvoiceagent.cerebrium.ai/ ๐Ÿ”ฅ I'd be super happy to give you a GPU grant to host it on a Space, it would allow more people to discover and use it!
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2024-07-04T13:13:13.000Z
2024-07-04T13:17:16.379Z
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/posts/victor/936208234528035
2,207
1
553468971807366
[ { "type": "text", "value": "I've created a space for chatting with Gemma 2 using llama.cpp", "raw": "I've created a space for chatting with Gemma 2 using llama.cpp", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ๐ŸŽ›๏ธ Choose between 27B IT and 9b IT models", "raw": "- ๐ŸŽ›๏ธ Choose between 27B IT and 9b IT models", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ๐Ÿš€ Fast inference using llama.cpp", "raw": "- ๐Ÿš€ Fast inference using llama.cpp", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/gokaygokay/Gemma-2-llamacpp", "resource": { "type": "space", "id": "gokaygokay/Gemma-2-llamacpp", "discussionNum": null }, "url": "https://huggingface.co/spaces/gokaygokay/Gemma-2-llamacpp", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
I've created a space for chatting with Gemma 2 using llama.cpp - ๐ŸŽ›๏ธ Choose between 27B IT and 9b IT models - ๐Ÿš€ Fast inference using llama.cpp - https://huggingface.co/spaces/gokaygokay/Gemma-2-llamacpp
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2024-07-04T11:16:41.000Z
2024-07-05T07:13:35.759Z
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/posts/gokaygokay/553468971807366
4,049
1
556266456592988
[ { "type": "mention", "value": null, "raw": "@Omartificial-Intelligence-Space", "resource": null, "url": null, "href": null, "user": "Omartificial-Intelligence-Space", "lang": null, "code": null, "label": null }, { "type": "text", "value": " has trained and released 6 Arabic embedding models for semantic similarity. 4 of them outperform all previous models on the STS17 Arabic-Arabic task! ", "raw": " has trained and released 6 Arabic embedding models for semantic similarity. 4 of them outperform all previous models on the STS17 Arabic-Arabic task! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“š Trained on a large dataset of 558k Arabic triplets translated from the AllNLI triplet dataset: ", "raw": "๐Ÿ“š Trained on a large dataset of 558k Arabic triplets translated from the AllNLI triplet dataset: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic-NLi-Triplet", "resource": { "type": "dataset", "id": "Omartificial-Intelligence-Space/Arabic-NLi-Triplet", "discussionNum": null }, "url": "https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic-NLi-Triplet", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "6๏ธโƒฃ 6 different base models: AraBERT, MarBERT, LaBSE, MiniLM, paraphrase-multilingual-mpnet-base, mpnet-base, ranging from 109M to 471M parameters.", "raw": "6๏ธโƒฃ 6 different base models: AraBERT, MarBERT, LaBSE, MiniLM, paraphrase-multilingual-mpnet-base, mpnet-base, ranging from 109M to 471M parameters.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿช† Trained with a Matryoshka loss, allowing you to truncate embeddings with minimal performance loss: smaller embeddings are faster to compare.", "raw": "๐Ÿช† Trained with a Matryoshka loss, allowing you to truncate embeddings with minimal performance loss: smaller embeddings are faster to compare.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ˆ Outperforms all commonly used multilingual models like ", "raw": "๐Ÿ“ˆ Outperforms all commonly used multilingual models like ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/intfloat/multilingual-e5-large", "resource": { "type": "model", "id": "intfloat/multilingual-e5-large", "discussionNum": null }, "url": "https://huggingface.co/intfloat/multilingual-e5-large", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ", ", "raw": ", ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "resource": { "type": "model", "id": "sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "discussionNum": null }, "url": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ", and ", "raw": ", and ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/sentence-transformers/LaBSE", "resource": { "type": "model", "id": "sentence-transformers/LaBSE", "discussionNum": null }, "url": "https://huggingface.co/sentence-transformers/LaBSE", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Check them out here:", "raw": "Check them out here:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": 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"https://huggingface.co/Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka", "resource": { "type": "model", "id": "Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka", "discussionNum": null }, "url": "https://huggingface.co/Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka", "resource": { "type": "model", "id": "Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka", "discussionNum": null }, "url": "https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Omartificial-Intelligence-Space/Arabic-labse-Matryoshka", "resource": { "type": "model", "id": "Omartificial-Intelligence-Space/Arabic-labse-Matryoshka", "discussionNum": null }, "url": "https://huggingface.co/Omartificial-Intelligence-Space/Arabic-labse-Matryoshka", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka", "resource": { "type": "model", "id": "Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka", "discussionNum": null }, "url": "https://huggingface.co/Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Omartificial-Intelligence-Space/Arabic-MiniLM-L12-v2-all-nli-triplet", "resource": { "type": "model", "id": "Omartificial-Intelligence-Space/Arabic-MiniLM-L12-v2-all-nli-triplet", "discussionNum": null }, "url": "https://huggingface.co/Omartificial-Intelligence-Space/Arabic-MiniLM-L12-v2-all-nli-triplet", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Or the collection with all: ", "raw": "Or the collection with all: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/Omartificial-Intelligence-Space/arabic-matryoshka-embedding-models-666f764d3b570f44d7f77d4e", "resource": { "type": "collection", "id": "Omartificial-Intelligence-Space/arabic-matryoshka-embedding-models-666f764d3b570f44d7f77d4e", "discussionNum": null }, "url": "https://huggingface.co/collections/Omartificial-Intelligence-Space/arabic-matryoshka-embedding-models-666f764d3b570f44d7f77d4e", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "My personal favourite is likely ", "raw": "My personal favourite is likely ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka", "resource": { "type": "model", "id": "Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka", "discussionNum": null }, "url": "https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ": a very efficient 135M parameters & scores #1 on ", "raw": ": a very efficient 135M parameters & scores #1 on ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/mteb/leaderboard", "resource": { "type": "space", "id": "mteb/leaderboard", "discussionNum": null }, "url": "https://huggingface.co/spaces/mteb/leaderboard", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": 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@Omartificial-Intelligence-Space has trained and released 6 Arabic embedding models for semantic similarity. 4 of them outperform all previous models on the STS17 Arabic-Arabic task! ๐Ÿ“š Trained on a large dataset of 558k Arabic triplets translated from the AllNLI triplet dataset: https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic-NLi-Triplet 6๏ธโƒฃ 6 different base models: AraBERT, MarBERT, LaBSE, MiniLM, paraphrase-multilingual-mpnet-base, mpnet-base, ranging from 109M to 471M parameters. ๐Ÿช† Trained with a Matryoshka loss, allowing you to truncate embeddings with minimal performance loss: smaller embeddings are faster to compare. ๐Ÿ“ˆ Outperforms all commonly used multilingual models like https://huggingface.co/intfloat/multilingual-e5-large, https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2, and https://huggingface.co/sentence-transformers/LaBSE. Check them out here: - https://huggingface.co/Omartificial-Intelligence-Space/Arabic-mpnet-base-all-nli-triplet - https://huggingface.co/Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka - https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka - https://huggingface.co/Omartificial-Intelligence-Space/Arabic-labse-Matryoshka - https://huggingface.co/Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka - https://huggingface.co/Omartificial-Intelligence-Space/Arabic-MiniLM-L12-v2-all-nli-triplet Or the collection with all: https://huggingface.co/collections/Omartificial-Intelligence-Space/arabic-matryoshka-embedding-models-666f764d3b570f44d7f77d4e My personal favourite is likely https://huggingface.co/Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka: a very efficient 135M parameters & scores #1 on https://huggingface.co/spaces/mteb/leaderboard.
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2024-07-04T10:40:53.000Z
2024-07-04T11:15:45.607Z
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/posts/tomaarsen/556266456592988
3,891
1
959222127501845
[ { "type": "text", "value": "I really like what the ", "raw": "I really like what the ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@jasperAITeam", "resource": null, "url": null, "href": null, "user": "jasperAITeam", "lang": null, "code": null, "label": null }, { "type": "text", "value": " designed with Flash LoRA. It works really well for something that generates so quickly, and I'm excited to test it out with Animate Diff, because I recently was testing LCM on it's own for AD and the results were already promising.", "raw": " designed with Flash LoRA. It works really well for something that generates so quickly, and I'm excited to test it out with Animate Diff, because I recently was testing LCM on it's own for AD and the results were already promising.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I put together my own page of models using their code and LoRA. Enjoy!", "raw": "I put together my own page of models using their code and LoRA. Enjoy!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/alvdansen/flash-lora-araminta-k-styles", "resource": { "type": "space", "id": "alvdansen/flash-lora-araminta-k-styles", "discussionNum": null }, "url": "https://huggingface.co/spaces/alvdansen/flash-lora-araminta-k-styles", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
I really like what the @jasperAITeam designed with Flash LoRA. It works really well for something that generates so quickly, and I'm excited to test it out with Animate Diff, because I recently was testing LCM on it's own for AD and the results were already promising. I put together my own page of models using their code and LoRA. Enjoy! https://huggingface.co/spaces/alvdansen/flash-lora-araminta-k-styles
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2024-07-04T08:56:02.000Z
2024-07-04T08:56:02.253Z
[]
/posts/alvdansen/959222127501845
2,969
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640888316825116
[ { "type": "text", "value": "Preview:", "raw": "Preview:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We will open source the 2.5B ChemVLM and the tool-enhanced ChemLLM-7B in the near future", "raw": "We will open source the 2.5B ChemVLM and the tool-enhanced ChemLLM-7B in the near future", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Preview: We will open source the 2.5B ChemVLM and the tool-enhanced ChemLLM-7B in the near future
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2024-07-04T08:44:27.000Z
2024-07-04T08:44:27.846Z
[]
/posts/qq8933/640888316825116
653
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[ { "type": "text", "value": "nanoLLaVA-1.5 is here! Same size (1B), better performance ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ", "raw": "nanoLLaVA-1.5 is here! Same size (1B), better performance ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "And it is much more powerful than v1.0", "raw": "And it is much more powerful than v1.0", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Try it out now on HF Spaces: ", "raw": "Try it out now on HF Spaces: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/qnguyen3/nanoLLaVA", "resource": { "type": "space", "id": "qnguyen3/nanoLLaVA", "discussionNum": null }, "url": "https://huggingface.co/spaces/qnguyen3/nanoLLaVA", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/qnguyen3/nanoLLaVA-1.5", "resource": { "type": "model", "id": "qnguyen3/nanoLLaVA-1.5", "discussionNum": null }, "url": "https://huggingface.co/qnguyen3/nanoLLaVA-1.5", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
nanoLLaVA-1.5 is here! Same size (1B), better performance ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ And it is much more powerful than v1.0 Try it out now on HF Spaces: https://huggingface.co/spaces/qnguyen3/nanoLLaVA Model: https://huggingface.co/qnguyen3/nanoLLaVA-1.5
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2024-07-04T07:29:49.000Z
2024-07-10T00:05:24.707Z
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/posts/qnguyen3/902515522013272
3,713
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Below we experiment with negative merger weighting (-1.0!) using task arithmetic. Merge formula on the model card and in the repo itself. This model is steered to behave opposite to what MopeyMule demonstrated. Based on the implications of the merge technique, we also propose Orthogonalized Vector Adaptation (OVA). We also extract a LoRA of the counter-refusal abliteration steering vector. The resulting merger is not a perfect model, but it's a behaviorally interesting model. The model name was inspired by a Philip K. Dick story. https://huggingface.co/grimjim/Llama-3-Perky-Pat-Instruct-8B Refusal vector weights ready for use: https://huggingface.co/grimjim/Llama-3-Instruct-abliteration-OVA-8B https://huggingface.co/grimjim/Llama-3-Instruct-abliteration-LoRA-8B
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2024-07-04T01:28:55.000Z
2024-07-07T06:25:43.794Z
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/posts/grimjim/856833400490073
2,261
3
446229141839176
[ { "type": "text", "value": "An Open-source and super-fast alternative to ", "raw": "An Open-source and super-fast alternative to ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@OpenAI", "resource": null, "url": null, "href": null, "user": "OpenAI", "lang": null, "code": null, "label": null }, { "type": "text", "value": " GPT4o is here! ๐Ÿš€", "raw": " GPT4o is here! ๐Ÿš€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "In November last year, Iliad announced a fully open-source-oriented AI lab called @kyutai_labs ๐Ÿงช", "raw": "In November last year, Iliad announced a fully open-source-oriented AI lab called @kyutai_labs ๐Ÿงช", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "In this very short time they have released Moshi! An open speech-to-speech model ๐Ÿ—ฃ๏ธ... released publicly even before closed GPT4o (yes, you can try it right now!) ๐ŸŒ", "raw": "In this very short time they have released Moshi! An open speech-to-speech model ๐Ÿ—ฃ๏ธ... released publicly even before closed GPT4o (yes, you can try it right now!) ๐ŸŒ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Demo: ", "raw": "Demo: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.moshi.chat/?queue_id=talktomoshi", "resource": null, "url": null, "href": "https://www.moshi.chat/?queue_id=talktomoshi", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This is what you expect an intelligent companion to do! ๐Ÿค– It is continuously generating responses and listening at the same time with sub 300 ms latency! โฑ๏ธ", "raw": "This is what you expect an intelligent companion to do! ๐Ÿค– It is continuously generating responses and listening at the same time with sub 300 ms latency! โฑ๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This level of engagement is so new, it almost feels like I am under pressure to keep up with it! ๐Ÿ˜…", "raw": "This level of engagement is so new, it almost feels like I am under pressure to keep up with it! ๐Ÿ˜…", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "While it hallucinates like crazy! ๐Ÿคฏ I think fundamentally this is what a true assistant would look like. And did I say they are going to open-source it? ๐Ÿ†“", "raw": "While it hallucinates like crazy! ๐Ÿคฏ I think fundamentally this is what a true assistant would look like. And did I say they are going to open-source it? ๐Ÿ†“", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Weights and full technical report are promised to be coming soon! ๐Ÿ“œ", "raw": "Weights and full technical report are promised to be coming soon! ๐Ÿ“œ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This is the work of an incredible but just 8 team members!! ๐Ÿ‘ฅ๐Ÿ‘", "raw": "This is the work of an incredible but just 8 team members!! ๐Ÿ‘ฅ๐Ÿ‘", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
An Open-source and super-fast alternative to @OpenAI GPT4o is here! ๐Ÿš€ In November last year, Iliad announced a fully open-source-oriented AI lab called @kyutai_labs ๐Ÿงช In this very short time they have released Moshi! An open speech-to-speech model ๐Ÿ—ฃ๏ธ... released publicly even before closed GPT4o (yes, you can try it right now!) ๐ŸŒ Demo: https://www.moshi.chat/?queue_id=talktomoshi This is what you expect an intelligent companion to do! ๐Ÿค– It is continuously generating responses and listening at the same time with sub 300 ms latency! โฑ๏ธ This level of engagement is so new, it almost feels like I am under pressure to keep up with it! ๐Ÿ˜… While it hallucinates like crazy! ๐Ÿคฏ I think fundamentally this is what a true assistant would look like. And did I say they are going to open-source it? ๐Ÿ†“ Weights and full technical report are promised to be coming soon! ๐Ÿ“œ This is the work of an incredible but just 8 team members!! ๐Ÿ‘ฅ๐Ÿ‘
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2024-07-03T22:52:31.000Z
2024-08-05T00:17:50.723Z
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/posts/singhsidhukuldeep/446229141839176
3,807
1
277412384046383
[ { "type": "text", "value": "Qualitative Emergence: The Paradox of Statistical AI in Language Comprehension", "raw": "Qualitative Emergence: The Paradox of Statistical AI in Language Comprehension", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://empereur-pirate.medium.com/qualitative-emergence-the-paradox-of-statistical-ai-in-language-comprehension-ccd1e221423e", "resource": null, "url": null, "href": "https://empereur-pirate.medium.com/qualitative-emergence-the-paradox-of-statistical-ai-in-language-comprehension-ccd1e221423e", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This text explores the nature of generative AI language models, highlighting their ability to produce coherent content despite being based on statistical processes. It discusses the role of random exploration in AI's impressive capabilities, while emphasizing that these models lack true understanding or subjectivity. The article contrasts AI's quantitative-to-qualitative approach with human cognitive development. It argues that AI serves as a tool for rewriting, analogical combination, and reasoned evaluation, rather than being truly intelligent. The text concludes by considering AI's potential applications in fields like law, stressing the importance of transparency and participatory models in AI development and use.", "raw": "This text explores the nature of generative AI language models, highlighting their ability to produce coherent content despite being based on statistical processes. It discusses the role of random exploration in AI's impressive capabilities, while emphasizing that these models lack true understanding or subjectivity. The article contrasts AI's quantitative-to-qualitative approach with human cognitive development. It argues that AI serves as a tool for rewriting, analogical combination, and reasoned evaluation, rather than being truly intelligent. The text concludes by considering AI's potential applications in fields like law, stressing the importance of transparency and participatory models in AI development and use.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Qualitative Emergence: The Paradox of Statistical AI in Language Comprehension https://empereur-pirate.medium.com/qualitative-emergence-the-paradox-of-statistical-ai-in-language-comprehension-ccd1e221423e This text explores the nature of generative AI language models, highlighting their ability to produce coherent content despite being based on statistical processes. It discusses the role of random exploration in AI's impressive capabilities, while emphasizing that these models lack true understanding or subjectivity. The article contrasts AI's quantitative-to-qualitative approach with human cognitive development. It argues that AI serves as a tool for rewriting, analogical combination, and reasoned evaluation, rather than being truly intelligent. The text concludes by considering AI's potential applications in fields like law, stressing the importance of transparency and participatory models in AI development and use.
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2024-07-03T19:08:18.000Z
2024-07-03T19:09:07.601Z
[]
/posts/Empereur-Pirate/277412384046383
632
0
500874361151661
[ { "type": "text", "value": "As we advance on the path towards true Artificial General Intelligence (AGI), it's crucial to recognize and address the limitations inherent in current technologies, particularly in large language models (LLMs) like those developed by OpenAI. While LLMs excel in processing and generating text, their capabilities are largely constrained to the domains of natural language understanding and generation. This poses significant limitations when dealing with more complex, abstract mathematical concepts such as topological analysis, 3D geometry, and homotopy type theory.", "raw": "As we advance on the path towards true Artificial General Intelligence (AGI), it's crucial to recognize and address the limitations inherent in current technologies, particularly in large language models (LLMs) like those developed by OpenAI. While LLMs excel in processing and generating text, their capabilities are largely constrained to the domains of natural language understanding and generation. This poses significant limitations when dealing with more complex, abstract mathematical concepts such as topological analysis, 3D geometry, and homotopy type theory.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Topological Analysis and 3D Geometry: LLMs currently do not possess the inherent ability to understand or interpret the spatial and geometric data that is critical in fields like robotics, architecture, and advanced physics. These models lack the capacity to visualize or manipulate three-dimensional objects or comprehend the underlying properties that govern these forms.", "raw": "Topological Analysis and 3D Geometry: LLMs currently do not possess the inherent ability to understand or interpret the spatial and geometric data that is critical in fields like robotics, architecture, and advanced physics. These models lack the capacity to visualize or manipulate three-dimensional objects or comprehend the underlying properties that govern these forms.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Homotopy Type Theory is a branch of mathematics that combines homotopy theory and type theory. Homotopy type theory provides tools for a more robust handling of equivalences and transformations, something that LLMs are not designed to handle directly.", "raw": "Homotopy Type Theory is a branch of mathematics that combines homotopy theory and type theory. Homotopy type theory provides tools for a more robust handling of equivalences and transformations, something that LLMs are not designed to handle directly.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "For the development of AGI, it is not sufficient to merely enhance existing models' capacities within their linguistic domains. Instead, a synthesis of symbolic AI with an understanding of homotopy type theory could pave the way. Symbolic AI, which manipulates symbols and performs logical operations, when combined with the abstract mathematical reasoning of homotopy type theory, could lead to breakthroughs in how machines understand and interact with the world.", "raw": "For the development of AGI, it is not sufficient to merely enhance existing models' capacities within their linguistic domains. Instead, a synthesis of symbolic AI with an understanding of homotopy type theory could pave the way. Symbolic AI, which manipulates symbols and performs logical operations, when combined with the abstract mathematical reasoning of homotopy type theory, could lead to breakthroughs in how machines understand and interact with the world.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "To address these limitations we have developed Tenzin, which is a one-of-a-kind model with a planned release date within the next 1-2 weeks . To learn more join the waitlist at ", "raw": "To address these limitations we have developed Tenzin, which is a one-of-a-kind model with a planned release date within the next 1-2 weeks . To learn more join the waitlist at ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://octave-x.com/", "resource": null, "url": null, "href": "https://octave-x.com/", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
As we advance on the path towards true Artificial General Intelligence (AGI), it's crucial to recognize and address the limitations inherent in current technologies, particularly in large language models (LLMs) like those developed by OpenAI. While LLMs excel in processing and generating text, their capabilities are largely constrained to the domains of natural language understanding and generation. This poses significant limitations when dealing with more complex, abstract mathematical concepts such as topological analysis, 3D geometry, and homotopy type theory. Topological Analysis and 3D Geometry: LLMs currently do not possess the inherent ability to understand or interpret the spatial and geometric data that is critical in fields like robotics, architecture, and advanced physics. These models lack the capacity to visualize or manipulate three-dimensional objects or comprehend the underlying properties that govern these forms. Homotopy Type Theory is a branch of mathematics that combines homotopy theory and type theory. Homotopy type theory provides tools for a more robust handling of equivalences and transformations, something that LLMs are not designed to handle directly. For the development of AGI, it is not sufficient to merely enhance existing models' capacities within their linguistic domains. Instead, a synthesis of symbolic AI with an understanding of homotopy type theory could pave the way. Symbolic AI, which manipulates symbols and performs logical operations, when combined with the abstract mathematical reasoning of homotopy type theory, could lead to breakthroughs in how machines understand and interact with the world. To address these limitations we have developed Tenzin, which is a one-of-a-kind model with a planned release date within the next 1-2 weeks . To learn more join the waitlist at https://octave-x.com/.
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2024-07-03T13:58:17.000Z
2024-09-14T06:16:07.192Z
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/posts/Tar9897/500874361151661
3,269
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521197112105525
[ { "type": "text", "value": "New #NVIDIA paper: Improving Hyperparameter Optimization with Checkpointed Model Weights", "raw": "New #NVIDIA paper: Improving Hyperparameter Optimization with Checkpointed Model Weights", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Hyperparameter optimization often dominates the cost of model design. So, we want cheap surrogate functions that approximate model performance to guide our search. Existing methods can train on optimization metadata โ€“ like a trajectory of losses โ€“ to build these surrogates.", "raw": "Hyperparameter optimization often dominates the cost of model design. So, we want cheap surrogate functions that approximate model performance to guide our search. Existing methods can train on optimization metadata โ€“ like a trajectory of losses โ€“ to build these surrogates.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "In our work, we add the ability to train our hyperparameter optimization surrogates on checkpointed model weights with a graph metanetwork. This allows us to leverage a large, pre-existing source of information that can featurize the architecture, dataset, losses, and optimization procedure.", "raw": "In our work, we add the ability to train our hyperparameter optimization surrogates on checkpointed model weights with a graph metanetwork. This allows us to leverage a large, pre-existing source of information that can featurize the architecture, dataset, losses, and optimization procedure.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”Project page: ", "raw": "๐Ÿ”Project page: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://research.nvidia.com/labs/toronto-ai/FMS/", "resource": null, "url": null, "href": "https://research.nvidia.com/labs/toronto-ai/FMS/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘จโ€๐Ÿ’ป Code for reproduction: ", "raw": "๐Ÿ‘จโ€๐Ÿ’ป Code for reproduction: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/NVlabs/forecasting-model-search", "resource": null, "url": null, "href": "https://github.com/NVlabs/forecasting-model-search", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“„ Full Paper: ", "raw": "๐Ÿ“„ Full Paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2406.18630", "resource": null, "url": null, "href": "https://arxiv.org/abs/2406.18630", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Our project was a collaboration between NVIDIAโ€™s Toronto AI Lab and the TAO team.", "raw": "Our project was a collaboration between NVIDIAโ€™s Toronto AI Lab and the TAO team.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Check out more work from Toronto AI Lab here: ", "raw": "Check out more work from Toronto AI Lab here: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://research.nvidia.com/labs/toronto-ai/", "resource": null, "url": null, "href": "https://research.nvidia.com/labs/toronto-ai/", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "You can view the TAO toolkit here: ", "raw": "You can view the TAO toolkit here: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://developer.nvidia.com/tao-toolkit", "resource": null, "url": null, "href": "https://developer.nvidia.com/tao-toolkit", "user": null, "lang": null, "code": null, "label": null } ]
New #NVIDIA paper: Improving Hyperparameter Optimization with Checkpointed Model Weights Hyperparameter optimization often dominates the cost of model design. So, we want cheap surrogate functions that approximate model performance to guide our search. Existing methods can train on optimization metadata โ€“ like a trajectory of losses โ€“ to build these surrogates. In our work, we add the ability to train our hyperparameter optimization surrogates on checkpointed model weights with a graph metanetwork. This allows us to leverage a large, pre-existing source of information that can featurize the architecture, dataset, losses, and optimization procedure. ๐Ÿ”Project page: https://research.nvidia.com/labs/toronto-ai/FMS/ ๐Ÿ‘จโ€๐Ÿ’ป Code for reproduction: https://github.com/NVlabs/forecasting-model-search ๐Ÿ“„ Full Paper: https://arxiv.org/abs/2406.18630 Our project was a collaboration between NVIDIAโ€™s Toronto AI Lab and the TAO team. Check out more work from Toronto AI Lab here: https://research.nvidia.com/labs/toronto-ai/ You can view the TAO toolkit here: https://developer.nvidia.com/tao-toolkit
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2024-07-03T03:46:49.000Z
2024-07-03T03:46:49.109Z
[]
/posts/lorraine2/521197112105525
2,391
0
529245585071670
[ { "type": "text", "value": "The Universal Checkpointing paper is out! ", "raw": "The Universal Checkpointing paper is out! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2406.18820", "resource": null, "url": null, "href": "https://arxiv.org/abs/2406.18820", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "If you remember the Bigscience BLOOM-176B training, Tunji Ruwase and I co-invented this technology for Megatron-Deepspeed in order to enable to quickly scale up and down node topology while continuing training.", "raw": "If you remember the Bigscience BLOOM-176B training, Tunji Ruwase and I co-invented this technology for Megatron-Deepspeed in order to enable to quickly scale up and down node topology while continuing training.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Since then the DeepSpeed team continued improving on that and it has now been fully integrated into Deepspeed.", "raw": "Since then the DeepSpeed team continued improving on that and it has now been fully integrated into Deepspeed.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The blog post is here: ", "raw": "The blog post is here: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ucp/README.md", "resource": null, "url": null, "href": "https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ucp/README.md", "user": null, "lang": null, "code": null, "label": null } ]
The Universal Checkpointing paper is out! https://arxiv.org/abs/2406.18820 If you remember the Bigscience BLOOM-176B training, Tunji Ruwase and I co-invented this technology for Megatron-Deepspeed in order to enable to quickly scale up and down node topology while continuing training. Since then the DeepSpeed team continued improving on that and it has now been fully integrated into Deepspeed. The blog post is here: https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ucp/README.md
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2024-07-03T00:04:08.000Z
2024-07-03T00:04:08.242Z
[]
/posts/stas/529245585071670
1,078
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749393864424720
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AI Agents Solved! Not a drill and not lying, 100% success rate with GPT 3.5 and Swarm algorithms for AI Agents. GPT 3.5 will create and execute the Swarm algorithms, and they will complete the API call 100% of the time. Here is the Colab, play with it yourself. I have a Github Repo for it all too: https://colab.research.google.com/drive/1EF_JmPidwoCd8tEgOChUt6kCX7TBLe20?usp=sharing
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2024-07-02T23:15:01.000Z
2024-07-07T03:38:52.451Z
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/posts/TuringsSolutions/749393864424720
1,909
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742878541266051
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I changed my mind about kaggle nb versions. They're goated
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2024-07-02T22:54:23.000Z
2024-07-02T22:54:23.892Z
[]
/posts/nroggendorff/742878541266051
733
0
344113970391875
[ { "type": "text", "value": "๐ŸŒŸ It's been about a week since ", "raw": "๐ŸŒŸ It's been about a week since ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@Google", "resource": null, "url": null, "href": null, "user": "Google", "lang": null, "code": null, "label": null }, { "type": "text", "value": " dropped Gemma 2 and now Gemma 2 27B is the highest-ranked open-source LLM on LMSYS Chatbot Arena Leaderboard, beating Meta Llama 3 70B and Alibaba Qwen 2 72B! ๐Ÿš€๐Ÿ’ช", "raw": " dropped Gemma 2 and now Gemma 2 27B is the highest-ranked open-source LLM on LMSYS Chatbot Arena Leaderboard, beating Meta Llama 3 70B and Alibaba Qwen 2 72B! ๐Ÿš€๐Ÿ’ช", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ” Here is what a week of Gemma looked like:", "raw": "๐Ÿ” Here is what a week of Gemma looked like:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "1๏ธโƒฃ First, it's a challenging model to run. The only reason I could find is soft-capping of logits within the attention for longer context optimizations. And no one was doing that before! ๐Ÿคฏ", "raw": "1๏ธโƒฃ First, it's a challenging model to run. The only reason I could find is soft-capping of logits within the attention for longer context optimizations. And no one was doing that before! ๐Ÿคฏ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "2๏ธโƒฃ Next, the technical report mentions a 2B model... where is it? ๐Ÿค”", "raw": "2๏ธโƒฃ Next, the technical report mentions a 2B model... where is it? ๐Ÿค”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "3๏ธโƒฃ Simple things like a context length of 8192 tokens, the Rotary Position Embeddings (RoPE), and the approximated GeGLU non-linearity are similar to earlier Gemma's. ๐Ÿ“๐Ÿ”„", "raw": "3๏ธโƒฃ Simple things like a context length of 8192 tokens, the Rotary Position Embeddings (RoPE), and the approximated GeGLU non-linearity are similar to earlier Gemma's. ๐Ÿ“๐Ÿ”„", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "4๏ธโƒฃ But a lot of new stuff is here like Local Sliding Window and Global Attention: they alternate between them at every layer... don't know why! ๐Ÿคทโ€โ™‚๏ธ and of course Logit soft-capping. ๐Ÿ’ก", "raw": "4๏ธโƒฃ But a lot of new stuff is here like Local Sliding Window and Global Attention: they alternate between them at every layer... don't know why! ๐Ÿคทโ€โ™‚๏ธ and of course Logit soft-capping. ๐Ÿ’ก", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "5๏ธโƒฃ On-Policy Distillation of Language Models - Knowledge Distillation: Leverage a larger teacher model to train a smaller model (the 9B model). ๐ŸŽ“โžก๏ธ๐Ÿ“š", "raw": "5๏ธโƒฃ On-Policy Distillation of Language Models - Knowledge Distillation: Leverage a larger teacher model to train a smaller model (the 9B model). ๐ŸŽ“โžก๏ธ๐Ÿ“š", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "6๏ธโƒฃ Model Merging: Combined average models from experiments run with different hyperparameters. ๐Ÿงชโš—๏ธ", "raw": "6๏ธโƒฃ Model Merging: Combined average models from experiments run with different hyperparameters. ๐Ÿงชโš—๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "7๏ธโƒฃ And borrowed Grouped-Query Attention (GQA) from ", "raw": "7๏ธโƒฃ And borrowed Grouped-Query Attention (GQA) from ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@Meta", "resource": null, "url": null, "href": null, "user": "Meta", "lang": null, "code": null, "label": null }, { "type": "text", "value": " Llama-3. ๐Ÿ”„๐Ÿค", "raw": " Llama-3. ๐Ÿ”„๐Ÿค", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“– One of the best articles on Gemma 2: ", "raw": "๐Ÿ“– One of the best articles on Gemma 2: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/gemma2", "resource": null, "url": null, "href": "https://huggingface.co/blog/gemma2", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“Š Technical report: ", "raw": "๐Ÿ“Š Technical report: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://storage.googleapis.com/deepmind-media/gemma/gemma-2-report.pdf", "resource": null, "url": null, "href": "https://storage.googleapis.com/deepmind-media/gemma/gemma-2-report.pdf", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”— Models: ", "raw": "๐Ÿ”— Models: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315", "resource": { "type": "collection", "id": "google/gemma-2-release-667d6600fd5220e7b967f315", "discussionNum": null }, "url": "https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐ŸŒŸ It's been about a week since @Google dropped Gemma 2 and now Gemma 2 27B is the highest-ranked open-source LLM on LMSYS Chatbot Arena Leaderboard, beating Meta Llama 3 70B and Alibaba Qwen 2 72B! ๐Ÿš€๐Ÿ’ช ๐Ÿ” Here is what a week of Gemma looked like: 1๏ธโƒฃ First, it's a challenging model to run. The only reason I could find is soft-capping of logits within the attention for longer context optimizations. And no one was doing that before! ๐Ÿคฏ 2๏ธโƒฃ Next, the technical report mentions a 2B model... where is it? ๐Ÿค” 3๏ธโƒฃ Simple things like a context length of 8192 tokens, the Rotary Position Embeddings (RoPE), and the approximated GeGLU non-linearity are similar to earlier Gemma's. ๐Ÿ“๐Ÿ”„ 4๏ธโƒฃ But a lot of new stuff is here like Local Sliding Window and Global Attention: they alternate between them at every layer... don't know why! ๐Ÿคทโ€โ™‚๏ธ and of course Logit soft-capping. ๐Ÿ’ก 5๏ธโƒฃ On-Policy Distillation of Language Models - Knowledge Distillation: Leverage a larger teacher model to train a smaller model (the 9B model). ๐ŸŽ“โžก๏ธ๐Ÿ“š 6๏ธโƒฃ Model Merging: Combined average models from experiments run with different hyperparameters. ๐Ÿงชโš—๏ธ 7๏ธโƒฃ And borrowed Grouped-Query Attention (GQA) from @Meta Llama-3. ๐Ÿ”„๐Ÿค ๐Ÿ“– One of the best articles on Gemma 2: https://huggingface.co/blog/gemma2 ๐Ÿ“Š Technical report: https://storage.googleapis.com/deepmind-media/gemma/gemma-2-report.pdf ๐Ÿ”— Models: https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315
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2024-07-02T22:34:43.000Z
2024-07-02T22:34:43.031Z
[]
/posts/singhsidhukuldeep/344113970391875
580
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927324136184813
[ { "type": "text", "value": "Hello!", "raw": "Hello!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I've been in the lab, I think one or two of you saw my furtive attempts to create a dolphinized 2b Gemma, which is still waiting for more funding. I get paid in a week. ", "raw": "I've been in the lab, I think one or two of you saw my furtive attempts to create a dolphinized 2b Gemma, which is still waiting for more funding. I get paid in a week. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "inline_code", "value": null, "raw": "`</3`", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": "</3", "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Once that funding ran out, I dropped my last pinch of API credits to work on this:", "raw": "Once that funding ran out, I dropped my last pinch of API credits to work on this:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/DigitalClockwork/spatial_instruct_v1", "resource": { "type": "dataset", "id": "DigitalClockwork/spatial_instruct_v1", "discussionNum": null }, "url": "https://huggingface.co/datasets/DigitalClockwork/spatial_instruct_v1", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It is an instruct database for spatial interactions with color tokens, i'm planning to tune a TBD model. Been experimenting with Gemma, but i'm welcome to ( smaller! ) model suggestions. If you think your favorite 0.5/0.75/1/2b can handle numbers, distances, or colors especially well, most especially community-enhanced models... I'm listening to the comments, intently!", "raw": "It is an instruct database for spatial interactions with color tokens, i'm planning to tune a TBD model. Been experimenting with Gemma, but i'm welcome to ( smaller! ) model suggestions. If you think your favorite 0.5/0.75/1/2b can handle numbers, distances, or colors especially well, most especially community-enhanced models... I'm listening to the comments, intently!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Have a great day, and enjoy! This was one fun! ๐Ÿค—", "raw": "Have a great day, and enjoy! This was one fun! ๐Ÿค—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "inline_code", "value": null, "raw": "`-<3`", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": "-<3", "label": null } ]
Hello! I've been in the lab, I think one or two of you saw my furtive attempts to create a dolphinized 2b Gemma, which is still waiting for more funding. I get paid in a week. `</3` Once that funding ran out, I dropped my last pinch of API credits to work on this: https://huggingface.co/datasets/DigitalClockwork/spatial_instruct_v1 It is an instruct database for spatial interactions with color tokens, i'm planning to tune a TBD model. Been experimenting with Gemma, but i'm welcome to ( smaller! ) model suggestions. If you think your favorite 0.5/0.75/1/2b can handle numbers, distances, or colors especially well, most especially community-enhanced models... I'm listening to the comments, intently! Have a great day, and enjoy! This was one fun! ๐Ÿค— `-<3`
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2024-07-02T16:36:29.000Z
2024-07-02T16:37:27.996Z
[]
/posts/MrOvkill/927324136184813
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[ { "type": "text", "value": "๐—ฌ๐—ผ๐˜‚ ๐—ฑ๐—ผ๐—ป'๐˜ ๐—ป๐—ฒ๐—ฒ๐—ฑ \"๐—ณ๐˜‚๐—ป๐—ฐ๐˜๐—ถ๐—ผ๐—ป ๐—ฐ๐—ฎ๐—น๐—น๐—ถ๐—ป๐—ด ๐—ณ๐—ถ๐—ป๐—ฒ-๐˜๐˜‚๐—ป๐—ถ๐—ป๐—ด\" ๐˜๐—ผ ๐—ฏ๐˜‚๐—ถ๐—น๐—ฑ ๐—ด๐—ผ๐—ผ๐—ฑ ๐—ฎ๐—ด๐—ฒ๐—ป๐˜๐˜€ โ›”", "raw": "๐—ฌ๐—ผ๐˜‚ ๐—ฑ๐—ผ๐—ป'๐˜ ๐—ป๐—ฒ๐—ฒ๐—ฑ \"๐—ณ๐˜‚๐—ป๐—ฐ๐˜๐—ถ๐—ผ๐—ป ๐—ฐ๐—ฎ๐—น๐—น๐—ถ๐—ป๐—ด ๐—ณ๐—ถ๐—ป๐—ฒ-๐˜๐˜‚๐—ป๐—ถ๐—ป๐—ด\" ๐˜๐—ผ ๐—ฏ๐˜‚๐—ถ๐—น๐—ฑ ๐—ด๐—ผ๐—ผ๐—ฑ ๐—ฎ๐—ด๐—ฒ๐—ป๐˜๐˜€ โ›”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It's trendy to share models \"fine-tuned for function calling\"; but from my observations, this fine-tuning is not necessary or sufficient to build good agent systems.", "raw": "It's trendy to share models \"fine-tuned for function calling\"; but from my observations, this fine-tuning is not necessary or sufficient to build good agent systems.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "To name only a few:", "raw": "To name only a few:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿฆโ€โฌ› Nexusflow/๐—ก๐—ฒ๐˜…๐˜‚๐˜€๐—ฅ๐—ฎ๐˜ƒ๐—ฒ๐—ป-๐—ฉ๐Ÿฎ-๐Ÿญ๐Ÿฏ๐—•", "raw": "๐Ÿฆโ€โฌ› Nexusflow/๐—ก๐—ฒ๐˜…๐˜‚๐˜€๐—ฅ๐—ฎ๐˜ƒ๐—ฒ๐—ป-๐—ฉ๐Ÿฎ-๐Ÿญ๐Ÿฏ๐—•", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โŒ˜ CohereForAI/๐—ฐ๐Ÿฐ๐—ฎ๐—ถ-๐—ฐ๐—ผ๐—บ๐—บ๐—ฎ๐—ป๐—ฑ-๐—ฟ-๐—ฝ๐—น๐˜‚๐˜€", "raw": "โŒ˜ CohereForAI/๐—ฐ๐Ÿฐ๐—ฎ๐—ถ-๐—ฐ๐—ผ๐—บ๐—บ๐—ฎ๐—ป๐—ฑ-๐—ฟ-๐—ฝ๐—น๐˜‚๐˜€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โ›ต๏ธ mistralai/๐— ๐—ถ๐˜…๐˜๐—ฟ๐—ฎ๐—น-๐Ÿด๐˜…๐Ÿฎ๐Ÿฎ๐—•-๐—œ๐—ป๐˜€๐˜๐—ฟ๐˜‚๐—ฐ๐˜-๐˜ƒ๐Ÿฌ.๐Ÿญ", "raw": "โ›ต๏ธ mistralai/๐— ๐—ถ๐˜…๐˜๐—ฟ๐—ฎ๐—น-๐Ÿด๐˜…๐Ÿฎ๐Ÿฎ๐—•-๐—œ๐—ป๐˜€๐˜๐—ฟ๐˜‚๐—ฐ๐˜-๐˜ƒ๐Ÿฌ.๐Ÿญ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "\"Fine-tuned for function-calling\" generally means \"fine-tuned to generate function calls in correct JSON for extremely simple tasks\". In other terms, it means \"improve the formatting of the tool calls\".", "raw": "\"Fine-tuned for function-calling\" generally means \"fine-tuned to generate function calls in correct JSON for extremely simple tasks\". In other terms, it means \"improve the formatting of the tool calls\".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Yet I discovered two things while improving Transformers Agents:", "raw": "Yet I discovered two things while improving Transformers Agents:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿง Even when used as JSON agents, these fine-tuned models don't perform very well", "raw": "๐Ÿง Even when used as JSON agents, these fine-tuned models don't perform very well", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ… ๐™‚๐™ค๐™ค๐™™ ๐™—๐™–๐™จ๐™š ๐™ข๐™ค๐™™๐™š๐™ก๐™จ ๐™ฅ๐™š๐™ง๐™›๐™ค๐™ง๐™ข ๐™—๐™š๐™ฉ๐™ฉ๐™š๐™ง ๐™ฌ๐™ž๐™ฉ๐™๐™ค๐™ช๐™ฉ ๐™–๐™ฃ๐™ฎ ๐™›๐™ž๐™ฃ๐™š-๐™ฉ๐™ช๐™ฃ๐™ž๐™ฃ๐™œ, ๐™Ÿ๐™ช๐™จ๐™ฉ ๐™ฅ๐™ก๐™–๐™ž๐™ฃ ๐™ฅ๐™ง๐™ค๐™ข๐™ฅ๐™ฉ๐™ž๐™ฃ๐™œ. (Llama-3-70B-Instruct, GPT-4o, Claude-3.5-Sonnet) ", "raw": "๐Ÿ… ๐™‚๐™ค๐™ค๐™™ ๐™—๐™–๐™จ๐™š ๐™ข๐™ค๐™™๐™š๐™ก๐™จ ๐™ฅ๐™š๐™ง๐™›๐™ค๐™ง๐™ข ๐™—๐™š๐™ฉ๐™ฉ๐™š๐™ง ๐™ฌ๐™ž๐™ฉ๐™๐™ค๐™ช๐™ฉ ๐™–๐™ฃ๐™ฎ ๐™›๐™ž๐™ฃ๐™š-๐™ฉ๐™ช๐™ฃ๐™ž๐™ฃ๐™œ, ๐™Ÿ๐™ช๐™จ๐™ฉ ๐™ฅ๐™ก๐™–๐™ž๐™ฃ ๐™ฅ๐™ง๐™ค๐™ข๐™ฅ๐™ฉ๐™ž๐™ฃ๐™œ. (Llama-3-70B-Instruct, GPT-4o, Claude-3.5-Sonnet) ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‡ The graph below shows the count of errors for my GPT-4o validation run on the GAIA benchmark: ๐™ฐ๐š๐šŽ๐š—๐š๐™ฟ๐šŠ๐š›๐šœ๐š’๐š—๐š๐™ด๐š›๐š›๐š˜๐š› and ๐™ฐ๐š๐šŽ๐š—๐š๐™ด๐šก๐šŽ๐šŒ๐šž๐š๐š’๐š˜๐š—๐™ด๐š›๐š›๐š˜๐š› are the ones caused by incorrect formatting.", "raw": "๐Ÿ‘‡ The graph below shows the count of errors for my GPT-4o validation run on the GAIA benchmark: ๐™ฐ๐š๐šŽ๐š—๐š๐™ฟ๐šŠ๐š›๐šœ๐š’๐š—๐š๐™ด๐š›๐š›๐š˜๐š› and ๐™ฐ๐š๐šŽ๐š—๐š๐™ด๐šก๐šŽ๐šŒ๐šž๐š๐š’๐š˜๐š—๐™ด๐š›๐š›๐š˜๐š› are the ones caused by incorrect formatting.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โžค As you can see, their count is already close to 0!", "raw": "โžค As you can see, their count is already close to 0!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "And given that GPT-4o is certainly not fine-tuned for our Code tool calling format, this shows that \"function calling fine-tuning\" is not necessary!", "raw": "And given that GPT-4o is certainly not fine-tuned for our Code tool calling format, this shows that \"function calling fine-tuning\" is not necessary!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The hardest thing to get right in an agent is still to ๐™ฅ๐™ก๐™–๐™ฃ ๐™œ๐™ค๐™ค๐™™ ๐™ฉ๐™–๐™จ๐™ -๐™จ๐™ค๐™ก๐™ซ๐™ž๐™ฃ๐™œ ๐™ฉ๐™ง๐™–๐™Ÿ๐™š๐™˜๐™ฉ๐™ค๐™ง๐™ž๐™š๐™จ ๐™ค๐™ซ๐™š๐™ง ๐™จ๐™š๐™ซ๐™š๐™ง๐™–๐™ก ๐™จ๐™ฉ๐™š๐™ฅ๐™จ.", "raw": "The hardest thing to get right in an agent is still to ๐™ฅ๐™ก๐™–๐™ฃ ๐™œ๐™ค๐™ค๐™™ ๐™ฉ๐™–๐™จ๐™ -๐™จ๐™ค๐™ก๐™ซ๐™ž๐™ฃ๐™œ ๐™ฉ๐™ง๐™–๐™Ÿ๐™š๐™˜๐™ฉ๐™ค๐™ง๐™ž๐™š๐™จ ๐™ค๐™ซ๐™š๐™ง ๐™จ๐™š๐™ซ๐™š๐™ง๐™–๐™ก ๐™จ๐™ฉ๐™š๐™ฅ๐™จ.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "To improve this, we could:", "raw": "To improve this, we could:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Use more powerful base models", "raw": "- Use more powerful base models", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Make tool calling datasets with complex solving trajectories", "raw": "- Make tool calling datasets with complex solving trajectories", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Use RL! cc ", "raw": "- Use RL! cc ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@lvwerra", "resource": null, "url": null, "href": null, "user": "lvwerra", "lang": null, "code": null, "label": null } ]
๐—ฌ๐—ผ๐˜‚ ๐—ฑ๐—ผ๐—ป'๐˜ ๐—ป๐—ฒ๐—ฒ๐—ฑ "๐—ณ๐˜‚๐—ป๐—ฐ๐˜๐—ถ๐—ผ๐—ป ๐—ฐ๐—ฎ๐—น๐—น๐—ถ๐—ป๐—ด ๐—ณ๐—ถ๐—ป๐—ฒ-๐˜๐˜‚๐—ป๐—ถ๐—ป๐—ด" ๐˜๐—ผ ๐—ฏ๐˜‚๐—ถ๐—น๐—ฑ ๐—ด๐—ผ๐—ผ๐—ฑ ๐—ฎ๐—ด๐—ฒ๐—ป๐˜๐˜€ โ›” It's trendy to share models "fine-tuned for function calling"; but from my observations, this fine-tuning is not necessary or sufficient to build good agent systems. To name only a few: ๐Ÿฆโ€โฌ› Nexusflow/๐—ก๐—ฒ๐˜…๐˜‚๐˜€๐—ฅ๐—ฎ๐˜ƒ๐—ฒ๐—ป-๐—ฉ๐Ÿฎ-๐Ÿญ๐Ÿฏ๐—• โŒ˜ CohereForAI/๐—ฐ๐Ÿฐ๐—ฎ๐—ถ-๐—ฐ๐—ผ๐—บ๐—บ๐—ฎ๐—ป๐—ฑ-๐—ฟ-๐—ฝ๐—น๐˜‚๐˜€ โ›ต๏ธ mistralai/๐— ๐—ถ๐˜…๐˜๐—ฟ๐—ฎ๐—น-๐Ÿด๐˜…๐Ÿฎ๐Ÿฎ๐—•-๐—œ๐—ป๐˜€๐˜๐—ฟ๐˜‚๐—ฐ๐˜-๐˜ƒ๐Ÿฌ.๐Ÿญ "Fine-tuned for function-calling" generally means "fine-tuned to generate function calls in correct JSON for extremely simple tasks". In other terms, it means "improve the formatting of the tool calls". Yet I discovered two things while improving Transformers Agents: ๐Ÿง Even when used as JSON agents, these fine-tuned models don't perform very well ๐Ÿ… ๐™‚๐™ค๐™ค๐™™ ๐™—๐™–๐™จ๐™š ๐™ข๐™ค๐™™๐™š๐™ก๐™จ ๐™ฅ๐™š๐™ง๐™›๐™ค๐™ง๐™ข ๐™—๐™š๐™ฉ๐™ฉ๐™š๐™ง ๐™ฌ๐™ž๐™ฉ๐™๐™ค๐™ช๐™ฉ ๐™–๐™ฃ๐™ฎ ๐™›๐™ž๐™ฃ๐™š-๐™ฉ๐™ช๐™ฃ๐™ž๐™ฃ๐™œ, ๐™Ÿ๐™ช๐™จ๐™ฉ ๐™ฅ๐™ก๐™–๐™ž๐™ฃ ๐™ฅ๐™ง๐™ค๐™ข๐™ฅ๐™ฉ๐™ž๐™ฃ๐™œ. (Llama-3-70B-Instruct, GPT-4o, Claude-3.5-Sonnet) ๐Ÿ‘‡ The graph below shows the count of errors for my GPT-4o validation run on the GAIA benchmark: ๐™ฐ๐š๐šŽ๐š—๐š๐™ฟ๐šŠ๐š›๐šœ๐š’๐š—๐š๐™ด๐š›๐š›๐š˜๐š› and ๐™ฐ๐š๐šŽ๐š—๐š๐™ด๐šก๐šŽ๐šŒ๐šž๐š๐š’๐š˜๐š—๐™ด๐š›๐š›๐š˜๐š› are the ones caused by incorrect formatting. โžค As you can see, their count is already close to 0! And given that GPT-4o is certainly not fine-tuned for our Code tool calling format, this shows that "function calling fine-tuning" is not necessary! The hardest thing to get right in an agent is still to ๐™ฅ๐™ก๐™–๐™ฃ ๐™œ๐™ค๐™ค๐™™ ๐™ฉ๐™–๐™จ๐™ -๐™จ๐™ค๐™ก๐™ซ๐™ž๐™ฃ๐™œ ๐™ฉ๐™ง๐™–๐™Ÿ๐™š๐™˜๐™ฉ๐™ค๐™ง๐™ž๐™š๐™จ ๐™ค๐™ซ๐™š๐™ง ๐™จ๐™š๐™ซ๐™š๐™ง๐™–๐™ก ๐™จ๐™ฉ๐™š๐™ฅ๐™จ. To improve this, we could: - Use more powerful base models - Make tool calling datasets with complex solving trajectories - Use RL! cc @lvwerra
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2024-07-02T12:36:08.000Z
2024-07-04T09:24:53.140Z
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/posts/m-ric/530578558895023
2,662
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603960516535019
[ { "type": "text", "value": "Here's my cool outputs from \"X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance\" article. Their technique transforms mesh objects based on textual prompts. Featured here are a dynamically stylized 3D representation of Cristiano Ronaldo and a diamond dragon. ", "raw": "Here's my cool outputs from \"X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance\" article. Their technique transforms mesh objects based on textual prompts. Featured here are a dynamically stylized 3D representation of Cristiano Ronaldo and a diamond dragon. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“„ paper: ", "raw": "๐Ÿ“„ paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2303.15764", "resource": { "type": "paper", "id": "2303.15764", "discussionNum": null }, "url": "https://huggingface.co/papers/2303.15764", "href": null, "user": null, "lang": null, "code": null, "label": "X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic\n Textual Guidance (2303.15764)" } ]
Here's my cool outputs from "X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance" article. Their technique transforms mesh objects based on textual prompts. Featured here are a dynamically stylized 3D representation of Cristiano Ronaldo and a diamond dragon. ๐Ÿ“„ paper: https://huggingface.co/papers/2303.15764
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2024-07-02T12:02:30.000Z
2024-07-02T12:05:04.129Z
[]
/posts/merterbak/603960516535019
1,780
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133421500055370
[ { "type": "text", "value": "๐Ÿฆ Do you remember IBIS? Not a fancy bird but the open challenge in Inferring Binding Specificities of unexplored human transcription factors. Check our site (", "raw": "๐Ÿฆ Do you remember IBIS? Not a fancy bird but the open challenge in Inferring Binding Specificities of unexplored human transcription factors. Check our site (", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://ibis.autosome.org/", "resource": null, "url": null, "href": "https://ibis.autosome.org/", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ") and have a sip of fresh news below.", "raw": ") and have a sip of fresh news below.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘ฅ More than 100 teams registered for the challenge yet only two dozen are using the opportunity to explore their models on the Leaderboard. Don't miss the chance to participate in the Leaderboard stage, although independently of that you can submit the final solution.", "raw": "๐Ÿ‘ฅ More than 100 teams registered for the challenge yet only two dozen are using the opportunity to explore their models on the Leaderboard. Don't miss the chance to participate in the Leaderboard stage, although independently of that you can submit the final solution.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒ Remember, the training data for Leaderboard and Final are available online, and you are free to mix-and-match it in any combination.", "raw": "๐ŸŒ Remember, the training data for Leaderboard and Final are available online, and you are free to mix-and-match it in any combination.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒŒ For Leaderboard, we have received 650 total submissions of AAA (advanced ML) and 296 PWM models (a whopping set of 6682 PWMs in total). ", "raw": "๐ŸŒŒ For Leaderboard, we have received 650 total submissions of AAA (advanced ML) and 296 PWM models (a whopping set of 6682 PWMs in total). ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ For PWMs, the baseline is left far behind, but some TFs remain tough nuts to be cracked (see the attached figure 1).", "raw": "๐Ÿš€ For PWMs, the baseline is left far behind, but some TFs remain tough nuts to be cracked (see the attached figure 1).", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ˆ For AAAs, there is a solid improvement over the best-submitted PWMs in A2G, but the G2A discipline remains unpopular (see the attached figure 2). Free hint: this is your chance! ", "raw": "๐Ÿ“ˆ For AAAs, there is a solid improvement over the best-submitted PWMs in A2G, but the G2A discipline remains unpopular (see the attached figure 2). Free hint: this is your chance! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’ก Another free hint: If your model tends to overfit given a limited set of data for some TFs don't forget to use reverse-complement and shift augmentations. Also, don't hesitate to use multitarget models i.e. predicting the binding of multiple TFs at the same time. ", "raw": "๐Ÿ’ก Another free hint: If your model tends to overfit given a limited set of data for some TFs don't forget to use reverse-complement and shift augmentations. Also, don't hesitate to use multitarget models i.e. predicting the binding of multiple TFs at the same time. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’ก Last but not least, try to combine knowledge from all accessible experiment types, especially for G2A discipline (ChIP-Seq & genomic HT-SELEX) in a single model! ", "raw": "๐Ÿ’ก Last but not least, try to combine knowledge from all accessible experiment types, especially for G2A discipline (ChIP-Seq & genomic HT-SELEX) in a single model! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ฃ Finally and importantly, following the requests from the community, we decided to EXTEND the Leaderboard until the final submission deadline. ", "raw": "๐Ÿ“ฃ Finally and importantly, following the requests from the community, we decided to EXTEND the Leaderboard until the final submission deadline. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ—“๏ธ The final submission deadline is also EXTENDED until Aug 15. The final submission form and details will be posted on the IBIS website in the first half of July, follow our Telegram group and mailing list (see the links at ", "raw": "๐Ÿ—“๏ธ The final submission deadline is also EXTENDED until Aug 15. The final submission form and details will be posted on the IBIS website in the first half of July, follow our Telegram group and mailing list (see the links at ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://ibis.autosome.org", "resource": null, "url": null, "href": "https://ibis.autosome.org", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ").", "raw": ").", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿฆ Do you remember IBIS? Not a fancy bird but the open challenge in Inferring Binding Specificities of unexplored human transcription factors. Check our site (https://ibis.autosome.org/) and have a sip of fresh news below. ๐Ÿ‘ฅ More than 100 teams registered for the challenge yet only two dozen are using the opportunity to explore their models on the Leaderboard. Don't miss the chance to participate in the Leaderboard stage, although independently of that you can submit the final solution. ๐ŸŒ Remember, the training data for Leaderboard and Final are available online, and you are free to mix-and-match it in any combination. ๐ŸŒŒ For Leaderboard, we have received 650 total submissions of AAA (advanced ML) and 296 PWM models (a whopping set of 6682 PWMs in total). ๐Ÿš€ For PWMs, the baseline is left far behind, but some TFs remain tough nuts to be cracked (see the attached figure 1). ๐Ÿ“ˆ For AAAs, there is a solid improvement over the best-submitted PWMs in A2G, but the G2A discipline remains unpopular (see the attached figure 2). Free hint: this is your chance! ๐Ÿ’ก Another free hint: If your model tends to overfit given a limited set of data for some TFs don't forget to use reverse-complement and shift augmentations. Also, don't hesitate to use multitarget models i.e. predicting the binding of multiple TFs at the same time. ๐Ÿ’ก Last but not least, try to combine knowledge from all accessible experiment types, especially for G2A discipline (ChIP-Seq & genomic HT-SELEX) in a single model! ๐Ÿ“ฃ Finally and importantly, following the requests from the community, we decided to EXTEND the Leaderboard until the final submission deadline. ๐Ÿ—“๏ธ The final submission deadline is also EXTENDED until Aug 15. The final submission form and details will be posted on the IBIS website in the first half of July, follow our Telegram group and mailing list (see the links at https://ibis.autosome.org).
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2024-07-02T11:12:41.000Z
2024-07-02T11:12:41.422Z
[]
/posts/nikgr/133421500055370
1,558
0
753056979023615
[ { "type": "text", "value": "๐Ÿ”ฅ๐ŸŽญ๐ŸŒŸ New Research Alert - ECCV 2024 (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿ”ฅ", "raw": "๐Ÿ”ฅ๐ŸŽญ๐ŸŒŸ New Research Alert - ECCV 2024 (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿ”ฅ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“„ Title: Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture ๐Ÿ”", "raw": "๐Ÿ“„ Title: Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture ๐Ÿ”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ Description: Topo4D is a novel method for automated, high-fidelity 4D head tracking that optimizes dynamic topological meshes and 8K texture maps from multi-view time-series images.", "raw": "๐Ÿ“ Description: Topo4D is a novel method for automated, high-fidelity 4D head tracking that optimizes dynamic topological meshes and 8K texture maps from multi-view time-series images.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘ฅ Authors: ", "raw": "๐Ÿ‘ฅ Authors: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@Dazz1e", "resource": null, "url": null, "href": null, "user": "Dazz1e", "lang": null, "code": null, "label": null }, { "type": "text", "value": ", Y. Cheng, ", "raw": ", Y. Cheng, ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@Ryan-sjtu", "resource": null, "url": null, "href": null, "user": "Ryan-sjtu", "lang": null, "code": null, "label": null }, { "type": "text", "value": ", H. Jia, D. Xu, W. Zhu, Y. Yan", "raw": ", H. Jia, D. Xu, W. Zhu, Y. Yan", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“… Conference: ECCV, 29 Sep โ€“ 4 Oct, 2024 | Milano, Italy ๐Ÿ‡ฎ๐Ÿ‡น", "raw": "๐Ÿ“… Conference: ECCV, 29 Sep โ€“ 4 Oct, 2024 | Milano, Italy ๐Ÿ‡ฎ๐Ÿ‡น", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“„ Paper: ", "raw": "๐Ÿ“„ Paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2406.00440", "resource": { "type": "paper", "id": "2406.00440", "discussionNum": null }, "url": "https://huggingface.co/papers/2406.00440", "href": null, "user": null, "lang": null, "code": null, "label": "Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head\n Capture (2406.00440)" }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒ Github Page: ", "raw": "๐ŸŒ Github Page: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://xuanchenli.github.io/Topo4D/", "resource": null, "url": null, "href": "https://xuanchenli.github.io/Topo4D/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ Repository: ", "raw": "๐Ÿ“ Repository: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/XuanchenLi/Topo4D", "resource": null, "url": null, "href": "https://github.com/XuanchenLi/Topo4D", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ CVPR-2023-24-Papers: ", "raw": "๐Ÿš€ CVPR-2023-24-Papers: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/DmitryRyumin/CVPR-2023-24-Papers", "resource": null, "url": null, "href": "https://github.com/DmitryRyumin/CVPR-2023-24-Papers", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ WACV-2024-Papers: ", "raw": "๐Ÿš€ WACV-2024-Papers: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/DmitryRyumin/WACV-2024-Papers", "resource": null, "url": null, "href": "https://github.com/DmitryRyumin/WACV-2024-Papers", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ ICCV-2023-Papers: ", "raw": "๐Ÿš€ ICCV-2023-Papers: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/DmitryRyumin/ICCV-2023-Papers", "resource": null, "url": null, "href": "https://github.com/DmitryRyumin/ICCV-2023-Papers", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "raw": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "resource": { "type": "space", "id": "DmitryRyumin/NewEraAI-Papers", "discussionNum": null }, "url": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " curated by ", "raw": " curated by ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@DmitryRyumin", "resource": null, "url": null, "href": null, "user": "DmitryRyumin", "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ Added to the Avatars Collection: ", "raw": "๐Ÿš€ Added to the Avatars Collection: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "resource": { "type": "collection", "id": "DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "discussionNum": null }, "url": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ” Keywords: #Topo4D #4DHead #3DModeling #4DCapture #FacialAnimation #ComputerGraphics #MachineLearning #HighFidelity #TextureMapping #DynamicMeshes #GaussianSplatting #VisualEffects #ECCV2024", "raw": "๐Ÿ” Keywords: #Topo4D #4DHead #3DModeling #4DCapture #FacialAnimation #ComputerGraphics #MachineLearning #HighFidelity #TextureMapping #DynamicMeshes #GaussianSplatting #VisualEffects #ECCV2024", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿ”ฅ๐ŸŽญ๐ŸŒŸ New Research Alert - ECCV 2024 (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿ”ฅ ๐Ÿ“„ Title: Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture ๐Ÿ” ๐Ÿ“ Description: Topo4D is a novel method for automated, high-fidelity 4D head tracking that optimizes dynamic topological meshes and 8K texture maps from multi-view time-series images. ๐Ÿ‘ฅ Authors: @Dazz1e, Y. Cheng, @Ryan-sjtu, H. Jia, D. Xu, W. Zhu, Y. Yan ๐Ÿ“… Conference: ECCV, 29 Sep โ€“ 4 Oct, 2024 | Milano, Italy ๐Ÿ‡ฎ๐Ÿ‡น ๐Ÿ“„ Paper: https://huggingface.co/papers/2406.00440 ๐ŸŒ Github Page: https://xuanchenli.github.io/Topo4D/ ๐Ÿ“ Repository: https://github.com/XuanchenLi/Topo4D ๐Ÿš€ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers ๐Ÿš€ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers ๐Ÿš€ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers ๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin ๐Ÿš€ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36 ๐Ÿ” Keywords: #Topo4D #4DHead #3DModeling #4DCapture #FacialAnimation #ComputerGraphics #MachineLearning #HighFidelity #TextureMapping #DynamicMeshes #GaussianSplatting #VisualEffects #ECCV2024
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2024-07-02T08:40:06.000Z
2024-07-02T17:19:07.757Z
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[ { "type": "text", "value": "I've created a Stable Diffusion 3 (SD3) image generation space for convenience. Now you can:", "raw": "I've created a Stable Diffusion 3 (SD3) image generation space for convenience. Now you can:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "1. Generate SD3 prompts from images", "raw": "1. Generate SD3 prompts from images", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "2. Enhance your text prompts (turn 1-2 words into full SD3 prompts)", "raw": "2. Enhance your text prompts (turn 1-2 words into full SD3 prompts)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/gokaygokay/SD3-with-VLM-and-Prompt-Enhancer", "resource": null, "url": null, "href": "https://huggingface.co/spaces/gokaygokay/SD3-with-VLM-and-Prompt-Enhancer", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "These features are based on my custom models:", "raw": "These features are based on my custom models:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- VLM captioner for prompt generation:", "raw": "- VLM captioner for prompt generation:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " - ", "raw": " - ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/gokaygokay/sd3-long-captioner", "resource": { "type": "model", "id": "gokaygokay/sd3-long-captioner", "discussionNum": null }, "url": "https://huggingface.co/gokaygokay/sd3-long-captioner", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Prompt Enhancers for SD3 Models:", "raw": "- Prompt Enhancers for SD3 Models:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " - ", "raw": " - ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long", "resource": { "type": "model", "id": "gokaygokay/Lamini-Prompt-Enchance-Long", "discussionNum": null }, "url": "https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " - ", "raw": " - ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance", "resource": { "type": "model", "id": "gokaygokay/Lamini-Prompt-Enchance", "discussionNum": null }, "url": "https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "You can now simplify your SD3 workflow with these tools!", "raw": "You can now simplify your SD3 workflow with these tools!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
I've created a Stable Diffusion 3 (SD3) image generation space for convenience. Now you can: 1. Generate SD3 prompts from images 2. Enhance your text prompts (turn 1-2 words into full SD3 prompts) https://huggingface.co/spaces/gokaygokay/SD3-with-VLM-and-Prompt-Enhancer These features are based on my custom models: - VLM captioner for prompt generation: - https://huggingface.co/gokaygokay/sd3-long-captioner - Prompt Enhancers for SD3 Models: - https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long - https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance You can now simplify your SD3 workflow with these tools!
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2024-07-02T08:00:29.000Z
2024-07-02T08:01:24.914Z
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[ { "type": "text", "value": "Check out our new benchmark paper on LLM agents for global events forecasting! ", "raw": "Check out our new benchmark paper on LLM agents for global events forecasting! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2407.01231", "resource": { "type": "paper", "id": "2407.01231", "discussionNum": null }, "url": "https://huggingface.co/papers/2407.01231", "href": null, "user": null, "lang": null, "code": null, "label": "MIRAI: Evaluating LLM Agents for Event Forecasting (2407.01231)" }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“œ Arxiv: ", "raw": "๐Ÿ“œ Arxiv: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2407.01231", "resource": null, "url": null, "href": "https://arxiv.org/abs/2407.01231", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”— Project page: ", "raw": "๐Ÿ”— Project page: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://mirai-llm.github.io", "resource": null, "url": null, "href": "https://mirai-llm.github.io", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’ป GitHub Repo: ", "raw": "๐Ÿ’ป GitHub Repo: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/yecchen/MIRAI", "resource": null, "url": null, "href": "https://github.com/yecchen/MIRAI", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ Dataset: ", "raw": "๐Ÿ“ Dataset: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://drive.google.com/file/d/1xmSEHZ_wqtBu1AwLpJ8wCDYmT-jRpfrN/view?usp=sharing", "resource": null, "url": null, "href": "https://drive.google.com/file/d/1xmSEHZ_wqtBu1AwLpJ8wCDYmT-jRpfrN/view?usp=sharing", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“Š Interactive Demo Notebook: ", "raw": "๐Ÿ“Š Interactive Demo Notebook: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/1QyqT35n6NbtPaNtqQ6A7ILG_GMeRgdnO?usp=sharing", "resource": null, "url": null, "href": "https://colab.research.google.com/drive/1QyqT35n6NbtPaNtqQ6A7ILG_GMeRgdnO?usp=sharing", "user": null, "lang": null, "code": null, "label": null } ]
Check out our new benchmark paper on LLM agents for global events forecasting! https://huggingface.co/papers/2407.01231 ๐Ÿ“œ Arxiv: https://arxiv.org/abs/2407.01231 ๐Ÿ”— Project page: https://mirai-llm.github.io ๐Ÿ’ป GitHub Repo: https://github.com/yecchen/MIRAI ๐Ÿ“ Dataset: https://drive.google.com/file/d/1xmSEHZ_wqtBu1AwLpJ8wCDYmT-jRpfrN/view?usp=sharing ๐Ÿ“Š Interactive Demo Notebook: https://colab.research.google.com/drive/1QyqT35n6NbtPaNtqQ6A7ILG_GMeRgdnO?usp=sharing
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2024-07-02T05:39:51.000Z
2024-07-02T05:44:20.105Z
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/posts/ydeng9/957785370728366
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LLMs are improving at math faster than my math coursework. I appreciate the all the hardworking engineers for helping me through high school.
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2024-07-02T04:45:54.000Z
2024-07-02T04:45:54.017Z
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/posts/puffy310/516704912173883
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469685030853998
[ { "type": "text", "value": "โœจ๐Ÿš€ Claude Sonnet 3.5 API. It's already weaving digital magic!", "raw": "โœจ๐Ÿš€ Claude Sonnet 3.5 API. It's already weaving digital magic!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿง ๐Ÿ’ป Try it at my space: ๐Ÿ”— ", "raw": "๐Ÿง ๐Ÿ’ป Try it at my space: ๐Ÿ”— ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/awacke1/AnthropicClaude3.5Sonnet-ACW", "resource": { "type": "space", "id": "awacke1/AnthropicClaude3.5Sonnet-ACW", "discussionNum": null }, "url": "https://huggingface.co/spaces/awacke1/AnthropicClaude3.5Sonnet-ACW", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Kudos to ", "raw": "Kudos to ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@AnthropicAI", "resource": null, "url": null, "href": null, "user": "AnthropicAI", "lang": null, "code": null, "label": null }, { "type": "text", "value": " for this elegant API! ๐Ÿ‘ #AI #CodeMagic #AnthropicAI Thanks Huggingface for hosting the best hub in the world for AI development!", "raw": " for this elegant API! ๐Ÿ‘ #AI #CodeMagic #AnthropicAI Thanks Huggingface for hosting the best hub in the world for AI development!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
โœจ๐Ÿš€ Claude Sonnet 3.5 API. It's already weaving digital magic! ๐Ÿง ๐Ÿ’ป Try it at my space: ๐Ÿ”— https://huggingface.co/spaces/awacke1/AnthropicClaude3.5Sonnet-ACW Kudos to @AnthropicAI for this elegant API! ๐Ÿ‘ #AI #CodeMagic #AnthropicAI Thanks Huggingface for hosting the best hub in the world for AI development!
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2024-07-02T01:43:24.000Z
2024-11-16T15:27:35.877Z
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/posts/awacke1/469685030853998
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260150398855068
[ { "type": "text", "value": "๐Ÿš€ Transformers are not here to take part but take over... and down goes real-time object detection! ๐Ÿ’ฅ", "raw": "๐Ÿš€ Transformers are not here to take part but take over... and down goes real-time object detection! ๐Ÿ’ฅ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Enter Real-time DEtection Transformer (RT-DETR) ๐Ÿฆพ as suggested capable of real-time object detection. ๐ŸŽฏ", "raw": "Enter Real-time DEtection Transformer (RT-DETR) ๐Ÿฆพ as suggested capable of real-time object detection. ๐ŸŽฏ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Object DEtection Transformer (DETR) is not new (", "raw": "Object DEtection Transformer (DETR) is not new (", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@Meta", "resource": null, "url": null, "href": null, "user": "Meta", "lang": null, "code": null, "label": null }, { "type": "text", "value": " did it eons ago) but it had the issue of every other transformer, high computational cost ๐Ÿ’ธ", "raw": " did it eons ago) but it had the issue of every other transformer, high computational cost ๐Ÿ’ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "RT-DETR brings an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed ๐ŸŽ๏ธ", "raw": "RT-DETR brings an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed ๐ŸŽ๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Gist is RT-DETR speeds up object detection by redesigning its encoder to process features more efficiently and selecting higher quality initial object queries. โšก", "raw": "Gist is RT-DETR speeds up object detection by redesigning its encoder to process features more efficiently and selecting higher quality initial object queries. โšก", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It also allows adjusting the number of decoder layers to balance speed and accuracy for different real-time scenarios. โš–๏ธ", "raw": "It also allows adjusting the number of decoder layers to balance speed and accuracy for different real-time scenarios. โš–๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This makes RT-DETR faster and more accurate than previous YOLO models. ๐Ÿ†", "raw": "This makes RT-DETR faster and more accurate than previous YOLO models. ๐Ÿ†", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "How much better๐Ÿ˜Ž/faster? โฑ๏ธ", "raw": "How much better๐Ÿ˜Ž/faster? โฑ๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "RT-DETR-R50 achieved 53.1% AP on COCO and 108 FPS on a T4 GPU, while RT-DETR-R101 achieved 54.3% AP and 74 FPS, outperforming advanced YOLO models in both speed and accuracy. ๐Ÿš€โœจ", "raw": "RT-DETR-R50 achieved 53.1% AP on COCO and 108 FPS on a T4 GPU, while RT-DETR-R101 achieved 54.3% AP and 74 FPS, outperforming advanced YOLO models in both speed and accuracy. ๐Ÿš€โœจ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“„ Paper: ", "raw": "๐Ÿ“„ Paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2304.08069", "resource": { "type": "paper", "id": "2304.08069", "discussionNum": null }, "url": "https://huggingface.co/papers/2304.08069", "href": null, "user": null, "lang": null, "code": null, "label": "DETRs Beat YOLOs on Real-time Object Detection (2304.08069)" }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿง  Models: ", "raw": "๐Ÿง  Models: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/models?search=pekingu/rt-detr", "resource": null, "url": null, "href": "https://huggingface.co/models?search=pekingu/rt-detr", "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿš€ Transformers are not here to take part but take over... and down goes real-time object detection! ๐Ÿ’ฅ Enter Real-time DEtection Transformer (RT-DETR) ๐Ÿฆพ as suggested capable of real-time object detection. ๐ŸŽฏ Object DEtection Transformer (DETR) is not new (@Meta did it eons ago) but it had the issue of every other transformer, high computational cost ๐Ÿ’ธ RT-DETR brings an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed ๐ŸŽ๏ธ Gist is RT-DETR speeds up object detection by redesigning its encoder to process features more efficiently and selecting higher quality initial object queries. โšก It also allows adjusting the number of decoder layers to balance speed and accuracy for different real-time scenarios. โš–๏ธ This makes RT-DETR faster and more accurate than previous YOLO models. ๐Ÿ† How much better๐Ÿ˜Ž/faster? โฑ๏ธ RT-DETR-R50 achieved 53.1% AP on COCO and 108 FPS on a T4 GPU, while RT-DETR-R101 achieved 54.3% AP and 74 FPS, outperforming advanced YOLO models in both speed and accuracy. ๐Ÿš€โœจ ๐Ÿ“„ Paper: https://huggingface.co/papers/2304.08069 ๐Ÿง  Models: https://huggingface.co/models?search=pekingu/rt-detr
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2024-07-01T22:32:05.000Z
2024-07-01T22:32:05.701Z
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/posts/singhsidhukuldeep/260150398855068
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