maxiw/hf-posts · Datasets at Hugging Face

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449943263175424	[ { "type": "text", "value": "next version of ", "raw": "next version of ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/sequelbox/Celestia", "href": null, "resource": { "type": "dataset", "id": "sequelbox/Celestia", "discussionNum": null }, "url": "https://huggingface.co/datasets/sequelbox/Celestia", "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": " will be ", "raw": " will be ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1", "href": null, "resource": { "type": "dataset", "id": "microsoft/orca-agentinstruct-1M-v1", "discussionNum": null }, "url": "https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1", "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": " style. coming soon", "raw": " style. coming soon", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]	next version of https://huggingface.co/datasets/sequelbox/Celestia will be https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1 style. coming soon	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/63444f2687964b331809eb55/WvZivsvKsM_t0tBtakovK.png", "fullname": "t.d.a.g.", "name": "sequelbox", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 50, "isFollowing": false }	[]	[]	[]	2024-11-17T18:26:14.000Z	2024-11-17T18:26:14.695Z	[]	/posts/sequelbox/449943263175424	13	0
194933978747638	[ { "type": "text", "value": "Minimalistic Adapters 🎃", "raw": "Minimalistic Adapters 🎃", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🚀Demo Here:", "raw": "🚀Demo Here:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": 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"https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32", "href": null, "resource": { "type": "collection", "id": "prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32", "discussionNum": null }, "url": "https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🚀For More Visit", "raw": "🚀For More Visit", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/strangerzonehf", "href": "https://huggingface.co/strangerzonehf", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": ".", "raw": ".", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": ".", "raw": ".", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": ".", "raw": ".", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🤗@prithivMLmods ", "raw": "🤗@prithivMLmods ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]	Minimalistic Adapters 🎃 🚀Demo Here: https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC 🚀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 🚀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 🚀For More Visit https://huggingface.co/strangerzonehf . . . 🤗@prithivMLmods	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/65bb837dbfb878f46c77de4c/UVtVbF_3rdt0DC8xTkpL1.jpeg", "fullname": "Prithiv Sakthi", "name": "prithivMLmods", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 342, "isFollowing": false }	[ { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/12Hjd_RsUd59yyHJOTDQj.png" }, { "type": "image", "url": 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982072243005650	[ { "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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "You can download all configs and full instructions", "raw": "You can download all configs and full instructions", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "> ", "raw": "> ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/112099700", "href": "https://www.patreon.com/posts/112099700", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": " - Fine Tuning post", "raw": " - Fine Tuning post", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "> ", "raw": "> ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/110879657", "href": "https://www.patreon.com/posts/110879657", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": " - LoRA post", "raw": " - LoRA post", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "You can download all configs and full instructions > ", "raw": "You can download all configs and full instructions > ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/112099700", "href": "https://www.patreon.com/posts/112099700", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "You can read the recent updates here : ", "raw": "You can read the recent updates here : ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/kohya-ss/sd-scripts/tree/sd3?tab=readme-ov-file#recent-updates", "href": "https://github.com/kohya-ss/sd-scripts/tree/sd3?tab=readme-ov-file#recent-updates", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "This is the Kohya GUI branch : ", "raw": "This is the Kohya GUI branch : ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/bmaltais/kohya_ss/tree/sd3-flux.1", "href": "https://github.com/bmaltais/kohya_ss/tree/sd3-flux.1", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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)", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 :)", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 :)	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1672531901326-6345bd89fe134dfd7a0dba40.png", "fullname": "Furkan Gözükara", "name": "MonsterMMORPG", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 368, "isFollowing": false }	[ { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/6345bd89fe134dfd7a0dba40/OLpWbbp__ZGrxkDvAku7a.jpeg" }, { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/6345bd89fe134dfd7a0dba40/Hn8LnZDOI6GVbz1NXho9Z.jpeg" } ]	[]	[ { "reaction": "🚀", "users": [ "MonsterMMORPG", "John6666" ], "count": 2 }, { "reaction": "❤️", "users": [ "MonsterMMORPG", "remjie" ], "count": 2 }, { "reaction": "🤗", "users": [ "MonsterMMORPG", "prithivMLmods" ], "count": 2 }, { "reaction": "🔥", "users": [ "MonsterMMORPG" ], "count": 1 }, { "reaction": "👀", "users": [ "MonsterMMORPG" ], "count": 1 }, { "reaction": "😎", "users": [ "MonsterMMORPG" ], "count": 1 }, { "reaction": "➕", "users": [ "MonsterMMORPG" ], "count": 1 }, { "reaction": "🧠", "users": [ "MonsterMMORPG" ], "count": 1 }, { "reaction": "👍", "users": [ "MonsterMMORPG" ], "count": 1 }, { "reaction": "🤝", "users": [ "MonsterMMORPG" ], "count": 1 }, { "reaction": "🤯", "users": [ "MonsterMMORPG" ], "count": 1 } ]	2024-11-17T14:49:39.000Z	2024-11-17T14:49:39.775Z	[]	/posts/MonsterMMORPG/982072243005650	263	0
953449438611686	[ { "type": "text", "value": "Ok RNNs can rap too:)", "raw": "Ok RNNs can rap too:)", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Here we implement the seminal RNN paper “Generating Text with Recurrent Neural Networks\"- we train a character-level multiplicative recurrent neural network model (~250k params) for 1000 epochs with Adam opt on 2pac's \"Hit 'em Up\", sample was fun lol.", "raw": "Here we implement the seminal RNN paper “Generating Text with Recurrent Neural Networks\"- we train a character-level multiplicative recurrent neural network model (~250k params) for 1000 epochs with Adam opt on 2pac's \"Hit 'em Up\", sample was fun lol.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Code: ", "raw": "Code: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/Jaykef/ai-algorithms/blob/main/generating_texts_with_rnns.ipynb", "href": "https://github.com/Jaykef/ai-algorithms/blob/main/generating_texts_with_rnns.ipynb", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]	Ok RNNs can rap too:) Here we implement the seminal RNN paper “Generating Text with Recurrent Neural Networks"- we train a character-level multiplicative recurrent neural network model (~250k params) for 1000 epochs with Adam opt on 2pac's "Hit 'em Up", sample was fun lol. Code: https://github.com/Jaykef/ai-algorithms/blob/main/generating_texts_with_rnns.ipynb	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6438a9027de34e8ea7e4b257/vib8QSd1AWMr_bR9ig_xJ.jpeg", "fullname": "Jaward Sesay", "name": "Jaward", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 189, "isFollowing": false }	[ { "type": "video", "url": "https://cdn-uploads.huggingface.co/production/uploads/6438a9027de34e8ea7e4b257/VLgYTC3kfxsoMHmKkD8Fo.mp4" }, { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/6438a9027de34e8ea7e4b257/4-z07k3Yar-e7-AKi_7Dh.png" }, { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/6438a9027de34e8ea7e4b257/v-KvBI3106FyIuVZMn_hr.png" } ]	[]	[ { "reaction": "👀", "users": [ "John6666" ], "count": 1 } ]	2024-11-17T13:02:24.000Z	2024-11-17T13:02:24.594Z	[]	/posts/Jaward/953449438611686	176	0
611948696998118	[ { "type": "text", "value": "Finaly I realesed mediapipe-face animation space.", "raw": "Finaly I realesed mediapipe-face animation space.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Mediapipe 68-points Eyes-Closed and Mouth-Opened", "raw": "Mediapipe 68-points Eyes-Closed and Mouth-Opened", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Akjava/mediapipe-68-facial-guide-eyes-closed-mouth-opened", "href": null, "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", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/Akjava/result-guide-image-eyes-mouth", "href": "https://huggingface.co/blog/Akjava/result-guide-image-eyes-mouth", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collections/Akjava/mediapipe-tools-672ffe8ee7b62763c31b70c7", "href": "https://huggingface.co/collections/Akjava/mediapipe-tools-672ffe8ee7b62763c31b70c7", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collections/Akjava/webp-3-frame-talking-animation-tools-672819ce4989f354cdbcc739", "href": "https://huggingface.co/collections/Akjava/webp-3-frame-talking-animation-tools-672819ce4989f354cdbcc739", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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	{ "avatarUrl": "/avatars/fb866e3758189d70488fc6a879151f45.svg", "fullname": "Akihito Miyazaki", "name": "Akjava", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 4, "isFollowing": false }	[]	[]	[ { "reaction": "👍", "users": [ "John6666" ], "count": 1 } ]	2024-11-17T12:43:13.000Z	2024-11-17T12:43:13.043Z	[]	/posts/Akjava/611948696998118	144	0
478756824597278	[ { "type": "text", "value": "Good folks at ", "raw": "Good folks at ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@nvidia", "href": null, "resource": null, "url": null, "user": "nvidia", "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Here is the Architecture & Implementation!", "raw": "Here is the Architecture & Implementation!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": ">> Core Components", "raw": ">> Core Components", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Model Foundation ", "raw": "Model Foundation ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Maintains original language capabilities while adding 3D generation ", "raw": "- Maintains original language capabilities while adding 3D generation ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Context length is set to 8,000 tokens ", "raw": "- Context length is set to 8,000 tokens ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "3D Representation Strategy ", "raw": "3D Representation Strategy ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Uses the OBJ file format for mesh representation ", "raw": "- Uses the OBJ file format for mesh representation ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Quantizes vertex coordinates into 64 discrete bins per axis ", "raw": "- Quantizes vertex coordinates into 64 discrete bins per axis ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Sorts faces by the lowest vertex indices for consistency ", "raw": "- Sorts faces by the lowest vertex indices for consistency ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Data Processing Pipeline ", "raw": "Data Processing Pipeline ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Applies random rotations (0°, 90°, 180°, 270°) for data augmentation ", "raw": "- Applies random rotations (0°, 90°, 180°, 270°) for data augmentation ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Generates ~125k mesh variations from 31k base meshes ", "raw": "- Generates ~125k mesh variations from 31k base meshes ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Uses Cap3D-generated captions for text descriptions ", "raw": "- Uses Cap3D-generated captions for text descriptions ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": ">> Training Framework", "raw": ">> Training Framework", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Dataset Composition ", "raw": "Dataset Composition ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- 40% Mesh Generation tasks ", "raw": "- 40% Mesh Generation tasks ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- 20% Mesh Understanding tasks ", "raw": "- 20% Mesh Understanding tasks ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- 40% General Conversation (UltraChat dataset) ", "raw": "- 40% General Conversation (UltraChat dataset) ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- 8x training turns for generation, 4x for understanding ", "raw": "- 8x training turns for generation, 4x for understanding ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Training Configuration ", "raw": "Training Configuration ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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) ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- 21,000 training iterations ", "raw": "- 21,000 training iterations ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Global batch size: 128 ", "raw": "- Global batch size: 128 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- AdamW optimizer with a 1e-5 learning rate ", "raw": "- AdamW optimizer with a 1e-5 learning rate ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- 30-step warmup with cosine scheduling ", "raw": "- 30-step warmup with cosine scheduling ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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) ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 (0°, 90°, 180°, 270°) 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!	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/662bf5bfe93bb73804ef9344/WXYLnjjJ4SROkoveIi7If.png", "fullname": "Kuldeep Singh Sidhu", "name": "singhsidhukuldeep", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 197, "isFollowing": false }	[ { "type": "video", "url": "https://cdn-uploads.huggingface.co/production/uploads/662bf5bfe93bb73804ef9344/7UzRyFrbCXT2wC_QDLKLx.mp4" } ]	[]	[ { "reaction": "🔥", "users": [ "Mrdesigner14", "John6666" ], "count": 2 }, { "reaction": "👍", "users": [ "csabakecskemeti", "gauravpatil" ], "count": 2 }, { "reaction": "🚀", "users": [ "John6666" ], "count": 1 } ]	2024-11-17T07:57:31.000Z	2024-11-17T07:57:31.455Z	[]	/posts/singhsidhukuldeep/478756824597278	380	0
578160125260008	[ { "type": "text", "value": "OmniVision-968M: a new local VLM for edge devices, fast & small but performant", "raw": "OmniVision-968M: a new local VLM for edge devices, fast & small but performant", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💨 a new vision language model with 9x less image tokens, super efficient ", "raw": "💨 a new vision language model with 9x less image tokens, super efficient ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📖 aligned with DPO for reducing hallucinations", "raw": "📖 aligned with DPO for reducing hallucinations", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "⚡️ Apache 2.0 license 🔥", "raw": "⚡️ Apache 2.0 license 🔥", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Demo hf.co/spaces/NexaAIDev/omnivlm-dpo-demo", "raw": "Demo hf.co/spaces/NexaAIDev/omnivlm-dpo-demo", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Model ", "raw": "Model ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/NexaAIDev/omnivision-968M", "href": null, "resource": { "type": "model", "id": "NexaAIDev/omnivision-968M", "discussionNum": null }, "url": "https://huggingface.co/NexaAIDev/omnivision-968M", "user": null, "label": null, "code": null, "lang": null } ]	OmniVision-968M: a new local VLM for edge devices, fast & small but performant 💨 a new vision language model with 9x less image tokens, super efficient 📖 aligned with DPO for reducing hallucinations ⚡️ Apache 2.0 license 🔥 Demo hf.co/spaces/NexaAIDev/omnivlm-dpo-demo Model https://huggingface.co/NexaAIDev/omnivision-968M	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1648113222875-6141a88b3a0ec78603c9e784.png", "fullname": "Merve Noyan", "name": "merve", "type": "user", "isPro": false, "isHf": true, "isMod": false, "followerCount": 5520, "isFollowing": false }	[ { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/6141a88b3a0ec78603c9e784/UpftcDUFh7eDXfvTbRROY.jpeg" } ]	[]	[ { "reaction": "🔥", "users": [ "YaTharThShaRma999", "John6666", "quyet7779", "typesdigital", "Csplk", "Norod78", "not-lain", "Sri-Vigneshwar-DJ", "ai-everyday" ], "count": 9 }, { "reaction": "👀", "users": [ "Csplk", "maxiw", "not-lain" ], "count": 3 }, { "reaction": "🤗", "users": [ "prithivMLmods" ], "count": 1 } ]	2024-11-16T23:26:19.000Z	2024-11-17T18:02:24.842Z	[ { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6048ea0c0f59ab4b614f1836/8Eg8IyPtJgOHmywcJ7E8a.jpeg", "fullname": "RITABRATA MAITI", "name": "ritabratamaiti", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 1, "isFollowing": false } ]	/posts/merve/578160125260008	1,104	1
269038377723431	[ { "type": "text", "value": "Kokoro: a small, fast 80M param TTS model hosted on ZeroGPU at ", "raw": "Kokoro: a small, fast 80M param TTS model hosted on ZeroGPU at ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://hf.co/spaces/hexgrad/Kokoro-TTS", "href": null, "resource": { "type": "space", "id": "hexgrad/Kokoro-TTS", "discussionNum": null }, "url": "https://hf.co/spaces/hexgrad/Kokoro-TTS", "user": null, "label": null, "code": null, "lang": null } ]	Kokoro: a small, fast 80M param TTS model hosted on ZeroGPU at https://hf.co/spaces/hexgrad/Kokoro-TTS	{ "avatarUrl": "/avatars/02074f60a2ef445a29343ed90a303cc6.svg", "fullname": "Hexgrad", "name": "hexgrad", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 9, "isFollowing": false }	[]	[]	[ { "reaction": "🔥", "users": [ "YaTharThShaRma999", "John6666", "Pendrokar", "Sri-Vigneshwar-DJ", "ai-everyday", "bendangelo", "ecyht2" ], "count": 7 } ]	2024-11-16T22:37:07.000Z	2024-11-17T14:32:24.785Z	[ { "avatarUrl": "/avatars/a1d86d990de3b90ed8fdb29c60337219.svg", "fullname": "Be", "name": "bendangelo", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 1, "isFollowing": false }, { "avatarUrl": "/avatars/52a153d04d325469e1be69bce610ebe5.svg", "fullname": "ecyht2", "name": "ecyht2", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 3, "isFollowing": false } ]	/posts/hexgrad/269038377723431	657	2
810523164589585	[ { "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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://pypi.org/project/llm-forwarder/", "href": "https://pypi.org/project/llm-forwarder/", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "More details", "raw": "More details", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://devquasar.com/llmforwarder/", "href": "https://devquasar.com/llmforwarder/", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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/	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/64e6d37e02dee9bcb9d9fa18/os24VYiNCoyth9yQSdv_A.jpeg", "fullname": "Csaba Kecskemeti", "name": "csabakecskemeti", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 5, "isFollowing": false }	[]	[]	[ { "reaction": "👀", "users": [ "John6666", "Sri-Vigneshwar-DJ" ], "count": 2 } ]	2024-11-16T21:58:39.000Z	2024-11-16T21:58:39.937Z	[]	/posts/csabakecskemeti/810523164589585	367	0
941904865732185	[ { "type": "text", "value": "Mann-E's new platform is up and running. ", "raw": "Mann-E's new platform is up and running. ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "You can access our platform here at ", "raw": "You can access our platform here at ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://mann-e.com", "href": "https://mann-e.com", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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. ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "What do you think?", "raw": "What do you think?", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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?	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/637251142f98dcc049b349de/kkRLjyaO55_nFrTNWRZFQ.jpeg", "fullname": "Haghiri", "name": "Muhammadreza", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 27, "isFollowing": false }	[]	[]	[ { "reaction": "🔥", "users": [ "Sri-Vigneshwar-DJ" ], "count": 1 } ]	2024-11-16T08:42:37.000Z	2024-11-16T16:39:47.790Z	[ { "avatarUrl": "/avatars/1d89852f84242051f859cdaf294e929a.svg", "fullname": "J Carl", "name": "jsan5344534", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": null, "isFollowing": false }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/637251142f98dcc049b349de/kkRLjyaO55_nFrTNWRZFQ.jpeg", "fullname": "Haghiri", "name": "Muhammadreza", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 27, "isFollowing": false } ]	/posts/Muhammadreza/941904865732185	588	3
911206794813112	[ { "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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "The system's architecture features three sophisticated components:", "raw": "The system's architecture features three sophisticated components:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "1. Editing Processor:", "raw": "1. Editing Processor:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Employs denoising score matching for training the control branch", "raw": "- Employs denoising score matching for training the control branch", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "2. Painting Assistor:", "raw": "2. Painting Assistor:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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)", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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)", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Features bounding box coordinate normalization for precise stroke localization", "raw": "- Features bounding box coordinate normalization for precise stroke localization", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "3. Idea Collector:", "raw": "3. Idea Collector:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Built as a modular ReactJS component library", "raw": "- Built as a modular ReactJS component library", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Supports cross-platform deployment via HTTP protocols", "raw": "- Supports cross-platform deployment via HTTP protocols", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Compatible with Gradio and ComfyUI frameworks", "raw": "- Compatible with Gradio and ComfyUI frameworks", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Features comprehensive layer management and parameter adjustment capabilities", "raw": "- Features comprehensive layer management and parameter adjustment capabilities", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Implements real-time canvas updates and preview generation", "raw": "- Implements real-time canvas updates and preview generation", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "What are your thoughts on AI-powered creative tools?", "raw": "What are your thoughts on AI-powered creative tools?", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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?	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/662bf5bfe93bb73804ef9344/WXYLnjjJ4SROkoveIi7If.png", "fullname": "Kuldeep Singh Sidhu", "name": "singhsidhukuldeep", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 197, "isFollowing": false }	[ { "type": "video", "url": "https://cdn-uploads.huggingface.co/production/uploads/662bf5bfe93bb73804ef9344/KQ1ZPxvjSFUGvqxHN1pFj.mp4" } ]	[]	[ { "reaction": "❤️", "users": [ "shafiquesial350", "Harbous", "John6666", "ethanker", "iojvsuynv" ], "count": 5 } ]	2024-11-16T05:49:12.000Z	2024-11-16T13:58:50.548Z	[ { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/Yv7lqS41OcvatDHlpxv59.png", "fullname": "Ronjeet", "name": "fff14", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": null, "isFollowing": false } ]	/posts/singhsidhukuldeep/911206794813112	1,287	2
849483557668478	[ { "type": "text", "value": "🕊️Hope🕊️ and ⚖️Justice⚖️ AI", "raw": "🕊️Hope🕊️ and ⚖️Justice⚖️ AI", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🚲 Stolen bike in Denver FOUND - Sometimes hope & justice DO prevail. ", "raw": "🚲 Stolen bike in Denver FOUND - Sometimes hope & justice DO prevail. ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🎬 So I Created an AI+Art+Music tribute: ", "raw": "🎬 So I Created an AI+Art+Music tribute: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": " -🧠 AI App that Evaluates GPT-4o vs Claude:", "raw": " -🧠 AI App that Evaluates GPT-4o vs Claude:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/awacke1/RescuerOfStolenBikes", "href": null, "resource": { "type": "space", "id": "awacke1/RescuerOfStolenBikes", "discussionNum": null }, "url": "https://huggingface.co/spaces/awacke1/RescuerOfStolenBikes", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "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", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```html\n<blockquote class=\"twitter-tweet\"><p lang=\"en\" dir=\"ltr\">QT your 🕊️Hope🕊️ and ⚖️Justice⚖️ art🎨<br><br>🚲 Stolen bike in Denver FOUND! <br> - Sometimes hope & justice DO prevail! <br><br>🎬 Created an AI+Art+Music tribute: <br> -🧠 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&ref_src=twsrc%5Etfw\">#GPT</a> <a href=\"https://twitter.com/hashtag/Claude?src=hash&ref_src=twsrc%5Etfw\">#Claude</a> <a href=\"https://twitter.com/hashtag/Huggingface?src=hash&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>— 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```", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": "<blockquote class=\"twitter-tweet\"><p lang=\"en\" dir=\"ltr\">QT your 🕊️Hope🕊️ and ⚖️Justice⚖️ art🎨<br><br>🚲 Stolen bike in Denver FOUND! <br> - Sometimes hope & justice DO prevail! <br><br>🎬 Created an AI+Art+Music tribute: <br> -🧠 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&ref_src=twsrc%5Etfw\">#GPT</a> <a href=\"https://twitter.com/hashtag/Claude?src=hash&ref_src=twsrc%5Etfw\">#Claude</a> <a href=\"https://twitter.com/hashtag/Huggingface?src=hash&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>— 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>", "lang": "html" }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "#GPT #Claude #Huggingface ", "raw": "#GPT #Claude #Huggingface ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@OpenAI", "href": null, "resource": null, "url": null, "user": "OpenAI", "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@AnthropicAI", "href": null, "resource": null, "url": null, "user": "AnthropicAI", "label": null, "code": null, "lang": null } ]	🕊️Hope🕊️ and ⚖️Justice⚖️ AI 🚲 Stolen bike in Denver FOUND - Sometimes hope & justice DO prevail. 🎬 So I Created an AI+Art+Music tribute: -🧠 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 🕊️Hope🕊️ and ⚖️Justice⚖️ art🎨<br><br>🚲 Stolen bike in Denver FOUND! <br> - Sometimes hope & justice DO prevail! <br><br>🎬 Created an AI+Art+Music tribute: <br> -🧠 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&ref_src=twsrc%5Etfw">#GPT</a> <a href="https://twitter.com/hashtag/Claude?src=hash&ref_src=twsrc%5Etfw">#Claude</a> <a href="https://twitter.com/hashtag/Huggingface?src=hash&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>— 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	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1656147940537-620630b603825909dcbeba35.jpeg", "fullname": "Aaron C Wacker", "name": "awacke1", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 184, "isFollowing": false }	[ { "type": "video", "url": "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/s_ioS7W-sTgeLL40HaZwU.mp4" }, { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/rrm2Bf7DXll7tViHftg5c.png" } ]	[]	[ { "reaction": "👍", "users": [ "John6666" ], "count": 1 } ]	2024-11-16T04:55:32.000Z	2024-11-16T05:15:15.661Z	[]	/posts/awacke1/849483557668478	397	0
443393273871393	[ { "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. ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/OMCRRueMhdI", "href": "https://youtu.be/OMCRRueMhdI", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/cA64Ix1vh75C7HoClUBhx.png", "fullname": "Richard A Aragon", "name": "TuringsSolutions", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 148, "isFollowing": false }	[]	[]	[ { "reaction": "😔", "users": [ "takeraparterer" ], "count": 1 } ]	2024-11-16T03:10:27.000Z	2024-11-17T07:42:58.935Z	[ { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6316fb937b0ee0136e5f1220/poHBoJ7QAF_s2CCaosdvQ.jpeg", "fullname": "Firstname Lastname", "name": "takeraparterer", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 29, "isFollowing": false }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/cA64Ix1vh75C7HoClUBhx.png", "fullname": "Richard A Aragon", "name": "TuringsSolutions", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 148, "isFollowing": false }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/Yv7lqS41OcvatDHlpxv59.png", "fullname": "Ronjeet", "name": "fff14", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": null, "isFollowing": false } ]	/posts/TuringsSolutions/443393273871393	327	6
580108358026949	[ { "type": "text", "value": "We’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": "We’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! ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🌟 Key Highlights:", "raw": "🌟 Key Highlights:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "\t•\tExpert Reviews: Aggregated from 3–6 reviews per paper, capturing the most insightful and constructive content.", "raw": "\t•\tExpert Reviews: Aggregated from 3–6 reviews per paper, capturing the most insightful and constructive content.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "\t•\tRich Metadata: we have aggregated the reviews, and also included full parsed paper", "raw": "\t•\tRich Metadata: we have aggregated the reviews, and also included full parsed paper", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "\t•\tLLM Ready: Perfect for fine-tuning (We did dpo and sft) ", "raw": "\t•\tLLM Ready: Perfect for fine-tuning (We did dpo and sft) ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🎯 Use Cases:", "raw": "🎯 Use Cases:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "\t•\tFine-tuning models with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT).", "raw": "\t•\tFine-tuning models with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT).", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "\t•\tBenchmarking zero-shot and long-context comprehension capabilities.", "raw": "\t•\tBenchmarking zero-shot and long-context comprehension capabilities.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔗 Explore the dataset: ", "raw": "🔗 Explore the dataset: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/alexshengzhili/Abstract2Appendix_v1_10k", "href": null, "resource": { "type": "dataset", "id": "alexshengzhili/Abstract2Appendix_v1_10k", "discussionNum": null }, "url": "https://huggingface.co/datasets/alexshengzhili/Abstract2Appendix_v1_10k", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "This dataset is based on the methodology described in our recent paper, “Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities”. Check it out for more details! ", "raw": "This dataset is based on the methodology described in our recent paper, “Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities”. Check it out for more details! ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2411.05232", "href": "https://arxiv.org/abs/2411.05232", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]	We’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! 🌟 Key Highlights: • Expert Reviews: Aggregated from 3–6 reviews per paper, capturing the most insightful and constructive content. • Rich Metadata: we have aggregated the reviews, and also included full parsed paper • LLM Ready: Perfect for fine-tuning (We did dpo and sft) 🎯 Use Cases: • Fine-tuning models with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT). • Benchmarking zero-shot and long-context comprehension capabilities. 🔗 Explore the dataset: https://huggingface.co/datasets/alexshengzhili/Abstract2Appendix_v1_10k This dataset is based on the methodology described in our recent paper, “Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities”. Check it out for more details! https://arxiv.org/abs/2411.05232	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/wthru065DlrO99caaTL2R.png", "fullname": "shengzhi alex li", "name": "alexshengzhili", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 5, "isFollowing": false }	[]	[]	[ { "reaction": "🔥", "users": [ "alexshengzhili", "John6666" ], "count": 2 } ]	2024-11-15T22:38:40.000Z	2024-11-16T13:59:54.538Z	[ { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/Yv7lqS41OcvatDHlpxv59.png", "fullname": "Ronjeet", "name": "fff14", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": null, "isFollowing": false } ]	/posts/alexshengzhili/580108358026949	909	1
632313037251618	[ { "type": "text", "value": "📢 For those who are interested in extracting information about ✍️ authors from texts, happy to share personal 📹 on Reading Between the lines: adapting ChatGPT-related systems 🤖 for Implicit Information Retrieval National ", "raw": "📢 For those who are interested in extracting information about ✍️ authors from texts, happy to share personal 📹 on Reading Between the lines: adapting ChatGPT-related systems 🤖 for Implicit Information Retrieval National ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Youtube: ", "raw": "Youtube: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/nXClX7EDYbE", "href": "https://youtu.be/nXClX7EDYbE", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔑 In this talk, we refer to IIR as such information that is indirectly expressed by ✍️ author / 👨 character / patient / any other entity.", "raw": "🔑 In this talk, we refer to IIR as such information that is indirectly expressed by ✍️ author / 👨 character / patient / any other entity.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📊 I cover the 1️⃣ pre-processing and 2️⃣ 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": "📊 I cover the 1️⃣ pre-processing and 2️⃣ 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "In pictures below, sharing the quick takeaways on the pipeline construction and experiment results 🧪", "raw": "In pictures below, sharing the quick takeaways on the pipeline construction and experiment results 🧪", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Related paper cards:", "raw": "Related paper cards:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📜 emotion-extraction: ", "raw": "📜 emotion-extraction: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://nicolay-r.github.io/#semeval2024-nicolay", "href": "https://nicolay-r.github.io/#semeval2024-nicolay", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📜 sentiment-analysis: ", "raw": "📜 sentiment-analysis: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://nicolay-r.github.io/#ljom2024", "href": "https://nicolay-r.github.io/#ljom2024", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Models:", "raw": "Models:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/nicolay-r/flan-t5-tsa-thor-base", "href": null, "resource": { "type": "model", "id": "nicolay-r/flan-t5-tsa-thor-base", "discussionNum": null }, "url": "https://huggingface.co/nicolay-r/flan-t5-tsa-thor-base", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base", "href": null, "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", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📓 PS: I got a hoppy for advetising HPMoR ✨ 😁 ", "raw": "📓 PS: I got a hoppy for advetising HPMoR ✨ 😁 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]	📢 For those who are interested in extracting information about ✍️ authors from texts, happy to share personal 📹 on Reading Between the lines: adapting ChatGPT-related systems 🤖 for Implicit Information Retrieval National Youtube: https://youtu.be/nXClX7EDYbE 🔑 In this talk, we refer to IIR as such information that is indirectly expressed by ✍️ author / 👨 character / patient / any other entity. 📊 I cover the 1️⃣ pre-processing and 2️⃣ 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 🧪 Related paper cards: 📜 emotion-extraction: https://nicolay-r.github.io/#semeval2024-nicolay 📜 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 📓 PS: I got a hoppy for advetising HPMoR ✨ 😁	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/64e62d11d27a8292c3637f86/aptDeBHpCJxcREj6KPLN1.jpeg", "fullname": "Nicolay Rusnachenko", "name": "nicolay-r", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 49, "isFollowing": false }	[ { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/64e62d11d27a8292c3637f86/Z3hMbd1IfSlGLAGx4YXch.png" }, { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/64e62d11d27a8292c3637f86/IFVbLeGPDqKyCau3iH6Z-.png" }, { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/64e62d11d27a8292c3637f86/nbwX8qLDzNTyjED4VUJNl.png" } ]	[]	[ { "reaction": "👀", "users": [ "John6666" ], "count": 1 } ]	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, ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "⚡ Optimized performance on Google Cloud's infrastructure, with TGI, TEI, and PyTorch acceleration.", "raw": "⚡ Optimized performance on Google Cloud's infrastructure, with TGI, TEI, and PyTorch acceleration.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🛠️ Hassle-free environment setup, no more dependency issues.", "raw": "🛠️ Hassle-free environment setup, no more dependency issues.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔄 Seamless updates to the latest stable versions.", "raw": "🔄 Seamless updates to the latest stable versions.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💼 Streamlined workflow, reducing dev and maintenance overheads.", "raw": "💼 Streamlined workflow, reducing dev and maintenance overheads.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔒 Robust security features of Google Cloud.", "raw": "🔒 Robust security features of Google Cloud.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "☁️ Fine-tuned for optimal performance, integrated with GKE and Vertex AI.", "raw": "☁️ Fine-tuned for optimal performance, integrated with GKE and Vertex AI.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📦 Community examples for easy experimentation and implementation.", "raw": "📦 Community examples for easy experimentation and implementation.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔜 TPU support for PyTorch Training/Inference and Text Generation Inference is coming soon!", "raw": "🔜 TPU support for PyTorch Training/Inference and Text Generation Inference is coming soon!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Find the documentation at ", "raw": "Find the documentation at ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/docs/google-cloud/en/index", "href": "https://huggingface.co/docs/google-cloud/en/index", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://discuss.huggingface.co/c/google-cloud/69", "href": "https://discuss.huggingface.co/c/google-cloud/69", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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: ⚡ Optimized performance on Google Cloud's infrastructure, with TGI, TEI, and PyTorch acceleration. 🛠️ Hassle-free environment setup, no more dependency issues. 🔄 Seamless updates to the latest stable versions. 💼 Streamlined workflow, reducing dev and maintenance overheads. 🔒 Robust security features of Google Cloud. ☁️ Fine-tuned for optimal performance, integrated with GKE and Vertex AI. 📦 Community examples for easy experimentation and implementation. 🔜 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	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/w3Z6xyKVBA6np65Tb16dP.jpeg", "fullname": "Simon Pagezy", "name": "pagezyhf", "type": "user", "isPro": false, "isHf": true, "isMod": false, "followerCount": 14, "isFollowing": false }	[]	[]	[ { "reaction": "👍", "users": [ "odin0", "John6666", "jcudit", "prithivMLmods", "qftop", "Sri-Vigneshwar-DJ" ], "count": 6 }, { "reaction": "🔥", "users": [ "Sri-Vigneshwar-DJ", "eskayML" ], "count": 2 } ]	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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "I wanted to introduce myself and my company ", "raw": "I wanted to introduce myself and my company ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Overlaiapp", "href": null, "resource": null, "url": null, "user": "Overlaiapp", "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": 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{ "type": "text", "value": "- ", "raw": "- ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/Overlaiai/SubArcticPolarBear", "href": null, "resource": { "type": "dataset", "id": "Overlaiai/SubArcticPolarBear", "discussionNum": null }, "url": "https://huggingface.co/datasets/Overlaiai/SubArcticPolarBear", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Overlai.ai Dataset Features", "raw": "Overlai.ai Dataset Features", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🎥 Oversampled: Every clip is captured in stunning 8K resolution, delivering rich detail ideal for fine tuning scenic landscapes and ocean dynamics.", "raw": "🎥 Oversampled: Every clip is captured in stunning 8K resolution, delivering rich detail ideal for fine tuning scenic landscapes and ocean dynamics.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📸 Variance: Includes close-up details, slow-motion footage of crashing waves, sweeping landscapes, and wildlife shots.", "raw": "📸 Variance: Includes close-up details, slow-motion footage of crashing waves, sweeping landscapes, and wildlife shots.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📋 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": "📋 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "⚙️ 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": "⚙️ 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🌅 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": "🌅 Light: Shot during early morning and sunset light for optimal color contrast and dynamic range, maximizing visual quality for color and lighting-sensitive tasks.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔍 Curation: Curated specifically for machine learning, providing clean, high-quality data for next generation model training.", "raw": "🔍 Curation: Curated specifically for machine learning, providing clean, high-quality data for next generation model training.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 🎥 Oversampled: Every clip is captured in stunning 8K resolution, delivering rich detail ideal for fine tuning scenic landscapes and ocean dynamics. 📸 Variance: Includes close-up details, slow-motion footage of crashing waves, sweeping landscapes, and wildlife shots. 📋 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. ⚙️ 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. 🌅 Light: Shot during early morning and sunset light for optimal color contrast and dynamic range, maximizing visual quality for color and lighting-sensitive tasks. 🔍 Curation: Curated specifically for machine learning, providing clean, high-quality data for next generation model training.	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/lJZriu6mJCgWkyYpbd4Pe.png", "fullname": "Luke Neumann", "name": "LukeNeumann", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 9, "isFollowing": false }	[]	[ { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/slG0zD_zwpPl4JsvBGWJ-.jpeg", "fullname": "Overlai.ai", "name": "Overlaiapp", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 1 } ]	[ { "reaction": "👍", "users": [ "Overlaiapp", "YaTharThShaRma999", "Ashish08", "Namaku", "John6666", "jgitsolutions" ], "count": 6 }, { "reaction": "🔥", "users": [ "prithivMLmods", "csabakecskemeti", "jeffcookio", "mmhamdy" ], "count": 4 } ]	2024-11-15T17:37:02.000Z	2024-11-15T17:37:28.190Z	[]	/posts/LukeNeumann/177552598965444	1,475	0
599942570828871	[ { "type": "text", "value": "Animaker / Anime Engine : 🤗", "raw": "Animaker / Anime Engine : 🤗", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Only on🚀: ", "raw": "Only on🚀: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/strangerzonehf", "href": "https://huggingface.co/strangerzonehf", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": 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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	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/65bb837dbfb878f46c77de4c/UVtVbF_3rdt0DC8xTkpL1.jpeg", "fullname": "Prithiv Sakthi", "name": "prithivMLmods", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 342, "isFollowing": false }	[ { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/G8uL0LgOKNnpQrosmAb1C.png" }, { "type": "image", "url": 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"https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/eDMeaAEUHAxs1qgUIzYMa.png" } ]	[ { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/65bb837dbfb878f46c77de4c/UVtVbF_3rdt0DC8xTkpL1.jpeg", "fullname": "Prithiv Sakthi", "name": "prithivMLmods", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 342 } ]	[ { "reaction": "🔥", "users": [ "darksfx", "hypergod", "ai4life44", "rdrede", "John6666", "Ngrthm", "MefhigosetH", "Tanvir1337", "RenderIo" ], "count": 9 }, { "reaction": "🤗", "users": [ "hypergod", "ai4life44", "Ngrthm", "RenderIo" ], "count": 4 }, { "reaction": "❤️", "users": [ "rdrede", "John6666", "Tanvir1337" ], "count": 3 }, { "reaction": "👍", "users": [ "ai4life44", "RenderIo" ], "count": 2 }, { "reaction": "➕", "users": [ "Ngrthm" ], "count": 1 } ]	2024-11-15T17:35:32.000Z	2024-11-16T03:59:23.710Z	[]	/posts/prithivMLmods/599942570828871	1,207	0
594210081206821	[ { "type": "text", "value": "𝗠𝗲𝘁𝗮 𝘁𝗲𝗮𝗺 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗪𝗮𝘁𝗲𝗿𝗺𝗮𝗿𝗸𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗮𝘁 𝗻𝗼𝘁 𝗲𝗱𝗶𝘁 𝗰𝗮𝗻 𝗯𝗿𝗲𝗮𝗸!🛡️", "raw": "𝗠𝗲𝘁𝗮 𝘁𝗲𝗮𝗺 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗪𝗮𝘁𝗲𝗿𝗺𝗮𝗿𝗸𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗮𝘁 𝗻𝗼𝘁 𝗲𝗱𝗶𝘁 𝗰𝗮𝗻 𝗯𝗿𝗲𝗮𝗸!🛡️", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🤔 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! 🎨 ", "raw": "🤔 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! 🎨 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🎭 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": "🎭 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "⛔ 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": "⛔ 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "A team of researchers at Meta was not happy with this. 😤", "raw": "A team of researchers at Meta was not happy with this. 😤", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🏗️ 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲", "raw": "🏗️ 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "▸ 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": "▸ 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "▸ [Only during training] The \"Augmenter\" randomly distorts the image: masks parts, crops, resizes, compresses. It's basically torture at this point.", "raw": "▸ [Only during training] The \"Augmenter\" randomly distorts the image: masks parts, crops, resizes, compresses. It's basically torture at this point.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "▸ 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": "▸ 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Gerat work ", "raw": "Gerat work ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@pierrefdz", "href": null, "resource": null, "url": null, "user": "pierrefdz", "label": null, "code": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@tomsander1998", "href": null, "resource": null, "url": null, "user": "tomsander1998", "label": null, "code": null, "lang": null }, { "type": "text", "value": "!", "raw": "!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Paper here 👉 ", "raw": "Paper here 👉 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2411.07231", "href": null, "resource": { "type": "paper", "id": "2411.07231", "discussionNum": null }, "url": "https://huggingface.co/papers/2411.07231", "user": null, "label": "Watermark Anything with Localized Messages (2411.07231)", "code": null, "lang": null } ]	𝗠𝗲𝘁𝗮 𝘁𝗲𝗮𝗺 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗪𝗮𝘁𝗲𝗿𝗺𝗮𝗿𝗸𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗮𝘁 𝗻𝗼𝘁 𝗲𝗱𝗶𝘁 𝗰𝗮𝗻 𝗯𝗿𝗲𝗮𝗸!🛡️ 🤔 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! 🎨 🎭 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. ⛔ 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. 😤 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! 🏗️ 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ▸ 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 ▸ [Only during training] The "Augmenter" randomly distorts the image: masks parts, crops, resizes, compresses. It's basically torture at this point. ▸ 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 👉 https://huggingface.co/papers/2411.07231	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/63d10d4e8eaa4831005e92b5/7p7-OmWM6PqqCs7ZStPGD.jpeg", "fullname": "Aymeric Roucher", "name": "m-ric", "type": "user", "isPro": false, "isHf": true, "isMod": false, "followerCount": 476, "isFollowing": false }	[ { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/63d10d4e8eaa4831005e92b5/d3SGNBRKF5HW56UT5TCML.png" } ]	[ { "avatarUrl": "/avatars/d6c00e7d98e5e8a52e99aa7b1a7815ee.svg", "fullname": "Pierre Fernandez", "name": "pierrefdz", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 8 }, { "avatarUrl": "/avatars/55f45f8ee6a4f477657cf0dd8409c533.svg", "fullname": "Tom Sander", "name": "tomsander1998", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 1 } ]	[ { "reaction": "👀", "users": [ "John6666" ], "count": 1 } ]	2024-11-15T17:23:08.000Z	2024-11-15T17:23:08.389Z	[]	/posts/m-ric/594210081206821	246	0
615920565093844	[ { "type": "text", "value": "Added ", "raw": "Added ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@amphion", "href": null, "resource": null, "url": null, "user": "amphion", "label": null, "code": null, "lang": null }, { "type": "text", "value": " MaskGCT & ", "raw": " MaskGCT & ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@hexgrad", "href": null, "resource": null, "url": null, "user": "hexgrad", "label": null, "code": null, "lang": 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. 🤞️🍀️", "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. 🤞️🍀️", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena", "href": null, "resource": { "type": "space", "id": "Pendrokar/TTS-Spaces-Arena", "discussionNum": null }, "url": "https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Svngoku/maskgct-audio-lab", "href": null, "resource": { "type": "space", "id": "Svngoku/maskgct-audio-lab", "discussionNum": null }, "url": "https://huggingface.co/spaces/Svngoku/maskgct-audio-lab", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/hexgrad/Kokoro-TTS", "href": null, "resource": { "type": "space", "id": "hexgrad/Kokoro-TTS", "discussionNum": null }, "url": "https://huggingface.co/spaces/hexgrad/Kokoro-TTS", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "I chose ", "raw": "I chose ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Svngoku", "href": null, "resource": null, "url": null, "user": "Svngoku", "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/amphion/maskgct", "href": null, "resource": { "type": "space", "id": "amphion/maskgct", "discussionNum": null }, "url": "https://huggingface.co/spaces/amphion/maskgct", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Had to remove ", "raw": "Had to remove ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@mrfakename", "href": null, "resource": null, "url": null, "user": "mrfakename", "label": null, "code": null, "lang": null }, { "type": "text", "value": " 's MetaVoice-1B Space from the available models as that space has been down for quite some time. 🤕️", "raw": " 's MetaVoice-1B Space from the available models as that space has been down for quite some time. 🤕️", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/mrfakename/MetaVoice-1B-v0.1", "href": null, "resource": { "type": "space", "id": "mrfakename/MetaVoice-1B-v0.1", "discussionNum": null }, "url": "https://huggingface.co/spaces/mrfakename/MetaVoice-1B-v0.1", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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. 🤞️🍀️ 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. 🤕️ 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!	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/63d52e0c4e5642795617f668/ztXLrdFz3gkUJUIIQXfHo.png", "fullname": "Yanis L", "name": "Pendrokar", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 13, "isFollowing": false }	[]	[ { "avatarUrl": "/avatars/02074f60a2ef445a29343ed90a303cc6.svg", "fullname": "Hexgrad", "name": "hexgrad", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 9 }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/62e54f0eae9d3f10acb95cb9/VAyk05hqB3OZWXEZW-B0q.png", "fullname": "mrfakename", "name": "mrfakename", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 948 }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6168218a4ed0b975c18f82a8/nUKzLtARCeevCl7wPgmMC.png", "fullname": "NIONGOLO Chrys Fé-Marty", "name": "Svngoku", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 22 } ]	[ { "reaction": "🔥", "users": [ "victor", "John6666" ], "count": 2 } ]	2024-11-15T16:52:51.000Z	2024-11-16T21:48:07.779Z	[ { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/5f17f0a0925b9863e28ad517/X7QKoiXbUtEZSG9jyvfk3.jpeg", "fullname": "Victor Mustar", "name": "victor", "type": "user", "isPro": true, "isHf": true, "isMod": false, "followerCount": 2578, "isFollowing": false } ]	/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 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`browser-intake-datadoghq.com`", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": "browser-intake-datadoghq.com", "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/64bce15bafd1e46c5504ad38/bQFX1iFbXEBXcQvUNL811.png", "fullname": "Di Zhang", "name": "qq8933", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 106, "isFollowing": false }	[]	[]	[ { "reaction": "👍", "users": [ "aust-t", "dingangui", "csabakecskemeti", "Csplk", "ai-everyday" ], "count": 5 }, { "reaction": "👀", "users": [ "John6666", "TouchNight" ], "count": 2 } ]	2024-11-04T15:09:31.000Z	2024-11-14T11:32:01.358Z	[ { "avatarUrl": "/avatars/b2725bb163fa15d6c5856121780d52eb.svg", "fullname": "Ci Splunk", "name": "Csplk", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 43, "isFollowing": false }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/64bce15bafd1e46c5504ad38/bQFX1iFbXEBXcQvUNL811.png", "fullname": "Di Zhang", "name": "qq8933", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 106, "isFollowing": false } ]	/posts/qq8933/376713378079900	2,237	3
380609696467893	[ { "type": "text", "value": "🎉 Celebrating One Year of #SauerkrautLM with Two Groundbreaking Releases! ", "raw": "🎉 Celebrating One Year of #SauerkrautLM with Two Groundbreaking Releases! ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-SFT", "href": null, "resource": { "type": "model", "id": "VAGOsolutions/SauerkrautLM-v2-14b-SFT", "discussionNum": null }, "url": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-SFT", "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO", "href": null, "resource": { "type": "model", "id": "VAGOsolutions/SauerkrautLM-v2-14b-DPO", "discussionNum": null }, "url": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO", "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔬 Technical Breakthroughs:", "raw": "🔬 Technical Breakthroughs:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Innovative three-phase Fine-Tuning approach", "raw": "💠 Innovative three-phase Fine-Tuning approach", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Two-step Spectrum SFT + one-step Spectrum DPO optimization phase for enhanced performance", "raw": "💠 Two-step Spectrum SFT + one-step Spectrum DPO optimization phase for enhanced performance", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Balance of German and English language capabilities", "raw": "💠 Balance of German and English language capabilities", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Advanced function calling - almost on par with Claude-3.5-Sonnet-20240620", "raw": "💠 Advanced function calling - almost on par with Claude-3.5-Sonnet-20240620", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🇩🇪 German Language Excellence:", "raw": "🇩🇪 German Language Excellence:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Enhance German language understanding and generation (SFT Version > DPO Version)", "raw": "💠 Enhance German language understanding and generation (SFT Version > DPO Version)", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Maintain authentic German linguistic nuances", "raw": "💠 Maintain authentic German linguistic nuances", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Improve cross-lingual capabilities", "raw": "💠 Improve cross-lingual capabilities", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Preserve cultural context awareness", "raw": "💠 Preserve cultural context awareness", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📊 Training Innovation:", "raw": "📊 Training Innovation:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Mathematics-focused content (proprietary classifier-selected)", "raw": "💠 Mathematics-focused content (proprietary classifier-selected)", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 High-quality German training data", "raw": "💠 High-quality German training data", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Specialized function calling datasets", "raw": "💠 Specialized function calling datasets", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Premium multilingual content", "raw": "💠 Premium multilingual content", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🎁 Community Contribution:", "raw": "🎁 Community Contribution:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "1️⃣ SauerkrautLM-Fermented-GER-DPO: 3,300 high-quality German training samples", "raw": "1️⃣ SauerkrautLM-Fermented-GER-DPO: 3,300 high-quality German training samples", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "2️⃣ SauerkrautLM-Fermented-Irrelevance-GER-DPO: 2,000 specialized samples for optimized function call irrelevance handling", "raw": "2️⃣ SauerkrautLM-Fermented-Irrelevance-GER-DPO: 2,000 specialized samples for optimized function call irrelevance handling", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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! 🚀", "raw": "Thank you to our incredible community and partners who have supported us throughout this journey. Here's to another year of AI innovation! 🚀", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]	🎉 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. 🔬 Technical Breakthroughs: 💠 Innovative three-phase Fine-Tuning approach 💠 Two-step Spectrum SFT + one-step Spectrum DPO optimization phase for enhanced performance 💠 Balance of German and English language capabilities 💠 Advanced function calling - almost on par with Claude-3.5-Sonnet-20240620 🇩🇪 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: 💠 Enhance German language understanding and generation (SFT Version > DPO Version) 💠 Maintain authentic German linguistic nuances 💠 Improve cross-lingual capabilities 💠 Preserve cultural context awareness 📊 Training Innovation: Our three-phase approach targeted specific layer percentages (15%, 20% and 25%) with carefully curated datasets, including: 💠 Mathematics-focused content (proprietary classifier-selected) 💠 High-quality German training data 💠 Specialized function calling datasets 💠 Premium multilingual content 🎁 Community Contribution: We're also releasing two new datasets in a few days: 1️⃣ SauerkrautLM-Fermented-GER-DPO: 3,300 high-quality German training samples 2️⃣ 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! 🚀	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/64b999a40b24527e9c25583a/xFHCewJdf5EGn8qDPypqy.jpeg", "fullname": "David Golchinfar", "name": "DavidGF", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 48, "isFollowing": false }	[ { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/64b999a40b24527e9c25583a/p2DKcLtfuRnsNBlsG6qSI.png" }, { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/64b999a40b24527e9c25583a/veWKzsQQtRA-RZ2A7-mIF.jpeg" }, { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/64b999a40b24527e9c25583a/mc782yi-uiF678nUsF1D2.jpeg" } ]	[]	[ { "reaction": "🔥", "users": [ "rizky-gumelar", "ZeroXClem", "John6666", "foscraft", "djuna", "not-lain" ], "count": 6 }, { "reaction": "👍", "users": [ "flozi00", "Jason233" ], "count": 2 } ]	2024-11-04T14:34:23.000Z	2024-11-04T14:35:08.561Z	[]	/posts/DavidGF/380609696467893	2,942	0
878599389112746	[ { "type": "text", "value": "Early Morning Before Work Project:", "raw": "Early Morning Before Work Project:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🌌 Introducing Cascade of Semantically Integrated Layers (CaSIL): A Humorously Over-Engineered Algorithm That Actually… Works 🤷‍♂️", "raw": "🌌 Introducing Cascade of Semantically Integrated Layers (CaSIL): A Humorously Over-Engineered Algorithm That Actually… Works 🤷‍♂️", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Let me introduce CaSIL – 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 that’s (surprisingly) meaningful to a human.", "raw": "Let me introduce CaSIL – 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 that’s (surprisingly) meaningful to a human.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "I’ve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. It’s built without agent frameworks—just good ol' Python and math—and it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Here’s a quick peek at the steps:", "raw": "I’ve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. It’s built without agent frameworks—just good ol' Python and math—and it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Here’s a quick peek at the steps:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "✨ How CaSIL Works:", "raw": "✨ How CaSIL Works:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Does it work? Yes! And in record time, too. Admittedly, the code is rough—two days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; it’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 rough—two days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; it’s a pure algorithm without complex dependencies, making it easy to integrate into your own LLM setups.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔗 Explore the repo here: ", "raw": "🔗 Explore the repo here: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers", "href": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📜 Example outputs: ", "raw": "📜 Example outputs: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers/blob/main/examples.md", "href": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers/blob/main/examples.md", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]	Early Morning Before Work Project: 🌌 Introducing Cascade of Semantically Integrated Layers (CaSIL): A Humorously Over-Engineered Algorithm That Actually… Works 🤷‍♂️ Let me introduce CaSIL – 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 that’s (surprisingly) meaningful to a human. I’ve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. It’s built without agent frameworks—just good ol' Python and math—and it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Here’s a quick peek at the steps: ✨ 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 rough—two days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; it’s a pure algorithm without complex dependencies, making it easy to integrate into your own LLM setups. 🔗 Explore the repo here: https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers 📜 Example outputs: https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers/blob/main/examples.md	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/64740cf7485a7c8e1bd51ac9/CXZCJm2x4ToT83pEIYyQR.png", "fullname": "Beckett Dillon", "name": "Severian", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 175, "isFollowing": false }	[]	[]	[ { "reaction": "👀", "users": [ "John6666" ], "count": 1 } ]	2024-11-04T14:19:39.000Z	2024-11-04T14:19:39.731Z	[]	/posts/Severian/878599389112746	452	0
915742231261639	[ { "type": "text", "value": "I just shipped ", "raw": "I just shipped ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`retrain-pipelines 0.1.1`", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": "retrain-pipelines 0.1.1", "lang": null }, { "type": "text", "value": " today. The doc is also pimped compared to previous release. That was clearly not mature then.", "raw": " today. The doc is also pimped compared to previous release. That was clearly not mature then.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "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?) !", "raw": "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?) !", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "In the meantime, you may enjoy retrying this :", "raw": "In the meantime, you may enjoy retrying this :", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/Aurelien-Morgan/stateful-metaflow-on-colab", "href": "https://huggingface.co/blog/Aurelien-Morgan/stateful-metaflow-on-colab", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]	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	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/651e93137b2a2e027f9e55df/5oXWJeEDCrMJLA4s_0I93.png", "fullname": "Aurélien-Morgan CLAUDON", "name": "Aurelien-Morgan", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 8, "isFollowing": false }	[ { "type": "image", "url": "https://cdn-uploads.huggingface.co/production/uploads/651e93137b2a2e027f9e55df/Uwll3V6Tnc7p6LQ2ac5mh.png" } ]	[]	[ { "reaction": "👀", "users": [ "John6666" ], "count": 1 } ]	2024-11-04T12:58:30.000Z	2024-11-04T12:58:30.527Z	[]	/posts/Aurelien-Morgan/915742231261639	446	0
169924015276572	[ { "type": "text", "value": "🙋🏻‍♂️hey there folks,", "raw": "🙋🏻‍♂️hey there folks,", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "periodic reminder : if you are experiencing ⚠️500 errors ⚠️ or ⚠️ abnormal ", "raw": "periodic reminder : if you are experiencing ⚠️500 errors ⚠️ or ⚠️ abnormal ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`spaces`", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": "spaces", "lang": null }, { "type": "text", "value": " behavior on load or launch ⚠️", "raw": " behavior on load or launch ⚠️", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "we have a thread 👉🏻 ", "raw": "we have a thread 👉🏻 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://discord.com/channels/879548962464493619/1295847667515129877", "href": "https://discord.com/channels/879548962464493619/1295847667515129877", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 🤗🤗🤗", "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 🤗🤗🤗", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]	🙋🏻‍♂️hey there folks, periodic reminder : if you are experiencing ⚠️500 errors ⚠️ or ⚠️ abnormal `spaces` behavior on load or launch ⚠️ we have a thread 👉🏻 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 🤗🤗🤗	{ "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/62a3bb1cd0d8c2c2169f0b88/eT2TS0IlQbZtz-F_zHLz9.jpeg", "fullname": "Joseph Pollack", "name": "Tonic", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 310, "isFollowing": false }	[]	[]	[ { "reaction": "👍", "users": [ "prithivMLmods", "Nymbo", "John6666", "AtAndDev", "clem", "eyov", "Zaws" ], "count": 7 }, { "reaction": "❤️", "users": [ "clem", "John6666" ], "count": 2 }, { "reaction": "👀", "users": [ "cstr" ], "count": 1 } ]	2024-11-04T11:41:55.000Z	2024-11-09T02:33:22.761Z	[ { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6640bbd0220cfa8cbfdce080/wiAHUu5ewawyipNs0YFBR.png", "fullname": "John Smith", "name": "John6666", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 384, "isFollowing": false } ]	/posts/Tonic/169924015276572	3,147	2

449943263175424

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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

[]

/posts/sequelbox/449943263175424

13

0

194933978747638

[ { "type": "text", "value": "Minimalistic Adapters 🎃", "raw": "Minimalistic Adapters 🎃", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🚀Demo Here:", "raw": "🚀Demo Here:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", 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Minimalistic Adapters 🎃 🚀Demo Here: https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC 🚀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 🚀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 🚀For More Visit https://huggingface.co/strangerzonehf . . . 🤗@prithivMLmods

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2024-11-17T17:14:32.000Z

2024-11-17T18:22:17.255Z

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119

2

982072243005650

[ { "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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "You can download all configs and full instructions", "raw": "You can download all configs and full instructions", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "> ", "raw": "> ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/112099700", "href": "https://www.patreon.com/posts/112099700", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": " - Fine Tuning post", "raw": " - Fine Tuning post", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "> ", "raw": "> ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/110879657", "href": "https://www.patreon.com/posts/110879657", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": " - LoRA post", "raw": " - LoRA post", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "You can download all configs and full instructions > ", "raw": "You can download all configs and full instructions > ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/112099700", "href": "https://www.patreon.com/posts/112099700", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "You can read the recent updates here : ", "raw": "You can read the recent updates here : ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/kohya-ss/sd-scripts/tree/sd3?tab=readme-ov-file#recent-updates", "href": "https://github.com/kohya-ss/sd-scripts/tree/sd3?tab=readme-ov-file#recent-updates", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "This is the Kohya GUI branch : ", "raw": "This is the Kohya GUI branch : ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/bmaltais/kohya_ss/tree/sd3-flux.1", "href": "https://github.com/bmaltais/kohya_ss/tree/sd3-flux.1", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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)", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 :)", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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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953449438611686

[ { "type": "text", "value": "Ok RNNs can rap too:)", "raw": "Ok RNNs can rap too:)", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Here we implement the seminal RNN paper “Generating Text with Recurrent Neural Networks\"- we train a character-level multiplicative recurrent neural network model (~250k params) for 1000 epochs with Adam opt on 2pac's \"Hit 'em Up\", sample was fun lol.", "raw": "Here we implement the seminal RNN paper “Generating Text with Recurrent Neural Networks\"- we train a character-level multiplicative recurrent neural network model (~250k params) for 1000 epochs with Adam opt on 2pac's \"Hit 'em Up\", sample was fun lol.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Code: ", "raw": "Code: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/Jaykef/ai-algorithms/blob/main/generating_texts_with_rnns.ipynb", "href": "https://github.com/Jaykef/ai-algorithms/blob/main/generating_texts_with_rnns.ipynb", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]

Ok RNNs can rap too:) Here we implement the seminal RNN paper “Generating Text with Recurrent Neural Networks"- we train a character-level multiplicative recurrent neural network model (~250k params) for 1000 epochs with Adam opt on 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

176

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611948696998118

[ { "type": "text", "value": "Finaly I realesed mediapipe-face animation space.", "raw": "Finaly I realesed mediapipe-face animation space.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Mediapipe 68-points Eyes-Closed and Mouth-Opened", "raw": "Mediapipe 68-points Eyes-Closed and Mouth-Opened", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Akjava/mediapipe-68-facial-guide-eyes-closed-mouth-opened", "href": null, "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", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/Akjava/result-guide-image-eyes-mouth", "href": "https://huggingface.co/blog/Akjava/result-guide-image-eyes-mouth", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collections/Akjava/mediapipe-tools-672ffe8ee7b62763c31b70c7", "href": "https://huggingface.co/collections/Akjava/mediapipe-tools-672ffe8ee7b62763c31b70c7", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collections/Akjava/webp-3-frame-talking-animation-tools-672819ce4989f354cdbcc739", "href": "https://huggingface.co/collections/Akjava/webp-3-frame-talking-animation-tools-672819ce4989f354cdbcc739", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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

0

478756824597278

[ { "type": "text", "value": "Good folks at ", "raw": "Good folks at ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@nvidia", "href": null, "resource": null, "url": null, "user": "nvidia", "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Here is the Architecture & Implementation!", "raw": "Here is the Architecture & Implementation!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": ">> Core Components", "raw": ">> Core Components", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Model Foundation ", "raw": "Model Foundation ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Maintains original language capabilities while adding 3D generation ", "raw": "- Maintains original language capabilities while adding 3D generation ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Context length is set to 8,000 tokens ", "raw": "- Context length is set to 8,000 tokens ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "3D Representation Strategy ", "raw": "3D Representation Strategy ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Uses the OBJ file format for mesh representation ", "raw": "- Uses the OBJ file format for mesh representation ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Quantizes vertex coordinates into 64 discrete bins per axis ", "raw": "- Quantizes vertex coordinates into 64 discrete bins per axis ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Sorts faces by the lowest vertex indices for consistency ", "raw": "- Sorts faces by the lowest vertex indices for consistency ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Data Processing Pipeline ", "raw": "Data Processing Pipeline ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Applies random rotations (0°, 90°, 180°, 270°) for data augmentation ", "raw": "- Applies random rotations (0°, 90°, 180°, 270°) for data augmentation ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Generates ~125k mesh variations from 31k base meshes ", "raw": "- Generates ~125k mesh variations from 31k base meshes ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Uses Cap3D-generated captions for text descriptions ", "raw": "- Uses Cap3D-generated captions for text descriptions ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": ">> Training Framework", "raw": ">> Training Framework", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Dataset Composition ", "raw": "Dataset Composition ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- 40% Mesh Generation tasks ", "raw": "- 40% Mesh Generation tasks ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- 20% Mesh Understanding tasks ", "raw": "- 20% Mesh Understanding tasks ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- 40% General Conversation (UltraChat dataset) ", "raw": "- 40% General Conversation (UltraChat dataset) ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- 8x training turns for generation, 4x for understanding ", "raw": "- 8x training turns for generation, 4x for understanding ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Training Configuration ", "raw": "Training Configuration ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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) ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- 21,000 training iterations ", "raw": "- 21,000 training iterations ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Global batch size: 128 ", "raw": "- Global batch size: 128 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- AdamW optimizer with a 1e-5 learning rate ", "raw": "- AdamW optimizer with a 1e-5 learning rate ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- 30-step warmup with cosine scheduling ", "raw": "- 30-step warmup with cosine scheduling ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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) ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 (0°, 90°, 180°, 270°) 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 💨 a new vision language model with 9x less image tokens, super efficient 📖 aligned with DPO for reducing hallucinations ⚡️ Apache 2.0 license 🔥 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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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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://pypi.org/project/llm-forwarder/", "href": "https://pypi.org/project/llm-forwarder/", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "More details", "raw": "More details", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://devquasar.com/llmforwarder/", "href": "https://devquasar.com/llmforwarder/", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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

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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. ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "You can access our platform here at ", "raw": "You can access our platform here at ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://mann-e.com", "href": "https://mann-e.com", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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. ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "What do you think?", "raw": "What do you think?", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "The system's architecture features three sophisticated components:", "raw": "The system's architecture features three sophisticated components:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "1. Editing Processor:", "raw": "1. Editing Processor:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Employs denoising score matching for training the control branch", "raw": "- Employs denoising score matching for training the control branch", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "2. Painting Assistor:", "raw": "2. Painting Assistor:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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)", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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)", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Features bounding box coordinate normalization for precise stroke localization", "raw": "- Features bounding box coordinate normalization for precise stroke localization", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "3. Idea Collector:", "raw": "3. Idea Collector:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Built as a modular ReactJS component library", "raw": "- Built as a modular ReactJS component library", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Supports cross-platform deployment via HTTP protocols", "raw": "- Supports cross-platform deployment via HTTP protocols", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Compatible with Gradio and ComfyUI frameworks", "raw": "- Compatible with Gradio and ComfyUI frameworks", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Features comprehensive layer management and parameter adjustment capabilities", "raw": "- Features comprehensive layer management and parameter adjustment capabilities", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- Implements real-time canvas updates and preview generation", "raw": "- Implements real-time canvas updates and preview generation", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "What are your thoughts on AI-powered creative tools?", "raw": "What are your thoughts on AI-powered creative tools?", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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": "🕊️Hope🕊️ and ⚖️Justice⚖️ AI", "raw": "🕊️Hope🕊️ and ⚖️Justice⚖️ AI", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🚲 Stolen bike in Denver FOUND - Sometimes hope & justice DO prevail. ", "raw": "🚲 Stolen bike in Denver FOUND - Sometimes hope & justice DO prevail. ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🎬 So I Created an AI+Art+Music tribute: ", "raw": "🎬 So I Created an AI+Art+Music tribute: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": " -🧠 AI App that Evaluates GPT-4o vs Claude:", "raw": " -🧠 AI App that Evaluates GPT-4o vs Claude:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/awacke1/RescuerOfStolenBikes", "href": null, "resource": { "type": "space", "id": "awacke1/RescuerOfStolenBikes", "discussionNum": null }, "url": "https://huggingface.co/spaces/awacke1/RescuerOfStolenBikes", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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", "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", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```html\n<blockquote class=\"twitter-tweet\"><p lang=\"en\" dir=\"ltr\">QT your 🕊️Hope🕊️ and ⚖️Justice⚖️ art🎨<br><br>🚲 Stolen bike in Denver FOUND! <br> - Sometimes hope & justice DO prevail! <br><br>🎬 Created an AI+Art+Music tribute: <br> -🧠 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&ref_src=twsrc%5Etfw\">#GPT</a> <a href=\"https://twitter.com/hashtag/Claude?src=hash&ref_src=twsrc%5Etfw\">#Claude</a> <a href=\"https://twitter.com/hashtag/Huggingface?src=hash&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>— 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```", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": "<blockquote class=\"twitter-tweet\"><p lang=\"en\" dir=\"ltr\">QT your 🕊️Hope🕊️ and ⚖️Justice⚖️ art🎨<br><br>🚲 Stolen bike in Denver FOUND! <br> - Sometimes hope & justice DO prevail! <br><br>🎬 Created an AI+Art+Music tribute: <br> -🧠 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&ref_src=twsrc%5Etfw\">#GPT</a> <a href=\"https://twitter.com/hashtag/Claude?src=hash&ref_src=twsrc%5Etfw\">#Claude</a> <a href=\"https://twitter.com/hashtag/Huggingface?src=hash&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>— 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>", "lang": "html" }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "#GPT #Claude #Huggingface ", "raw": "#GPT #Claude #Huggingface ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@OpenAI", "href": null, "resource": null, "url": null, "user": "OpenAI", "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@AnthropicAI", "href": null, "resource": null, "url": null, "user": "AnthropicAI", "label": null, "code": null, "lang": null } ]

🕊️Hope🕊️ and ⚖️Justice⚖️ AI 🚲 Stolen bike in Denver FOUND - Sometimes hope & justice DO prevail. 🎬 So I Created an AI+Art+Music tribute: -🧠 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 🕊️Hope🕊️ and ⚖️Justice⚖️ art🎨<br><br>🚲 Stolen bike in Denver FOUND! <br> - Sometimes hope & justice DO prevail! <br><br>🎬 Created an AI+Art+Music tribute: <br> -🧠 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&ref_src=twsrc%5Etfw">#GPT</a> <a href="https://twitter.com/hashtag/Claude?src=hash&ref_src=twsrc%5Etfw">#Claude</a> <a href="https://twitter.com/hashtag/Huggingface?src=hash&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>— 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

[]

/posts/awacke1/849483557668478

397

0

443393273871393

[ { "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. ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/OMCRRueMhdI", "href": "https://youtu.be/OMCRRueMhdI", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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": "We’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": "We’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! ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🌟 Key Highlights:", "raw": "🌟 Key Highlights:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "\t•\tExpert Reviews: Aggregated from 3–6 reviews per paper, capturing the most insightful and constructive content.", "raw": "\t•\tExpert Reviews: Aggregated from 3–6 reviews per paper, capturing the most insightful and constructive content.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "\t•\tRich Metadata: we have aggregated the reviews, and also included full parsed paper", "raw": "\t•\tRich Metadata: we have aggregated the reviews, and also included full parsed paper", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "\t•\tLLM Ready: Perfect for fine-tuning (We did dpo and sft) ", "raw": "\t•\tLLM Ready: Perfect for fine-tuning (We did dpo and sft) ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🎯 Use Cases:", "raw": "🎯 Use Cases:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "\t•\tFine-tuning models with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT).", "raw": "\t•\tFine-tuning models with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT).", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "\t•\tBenchmarking zero-shot and long-context comprehension capabilities.", "raw": "\t•\tBenchmarking zero-shot and long-context comprehension capabilities.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔗 Explore the dataset: ", "raw": "🔗 Explore the dataset: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/alexshengzhili/Abstract2Appendix_v1_10k", "href": null, "resource": { "type": "dataset", "id": "alexshengzhili/Abstract2Appendix_v1_10k", "discussionNum": null }, "url": "https://huggingface.co/datasets/alexshengzhili/Abstract2Appendix_v1_10k", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "This dataset is based on the methodology described in our recent paper, “Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities”. Check it out for more details! ", "raw": "This dataset is based on the methodology described in our recent paper, “Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities”. Check it out for more details! ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2411.05232", "href": "https://arxiv.org/abs/2411.05232", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]

We’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! 🌟 Key Highlights: • Expert Reviews: Aggregated from 3–6 reviews per paper, capturing the most insightful and constructive content. • Rich Metadata: we have aggregated the reviews, and also included full parsed paper • LLM Ready: Perfect for fine-tuning (We did dpo and sft) 🎯 Use Cases: • Fine-tuning models with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT). • Benchmarking zero-shot and long-context comprehension capabilities. 🔗 Explore the dataset: https://huggingface.co/datasets/alexshengzhili/Abstract2Appendix_v1_10k This dataset is based on the methodology described in our recent paper, “Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities”. 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": "📢 For those who are interested in extracting information about ✍️ authors from texts, happy to share personal 📹 on Reading Between the lines: adapting ChatGPT-related systems 🤖 for Implicit Information Retrieval National ", "raw": "📢 For those who are interested in extracting information about ✍️ authors from texts, happy to share personal 📹 on Reading Between the lines: adapting ChatGPT-related systems 🤖 for Implicit Information Retrieval National ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Youtube: ", "raw": "Youtube: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/nXClX7EDYbE", "href": "https://youtu.be/nXClX7EDYbE", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔑 In this talk, we refer to IIR as such information that is indirectly expressed by ✍️ author / 👨 character / patient / any other entity.", "raw": "🔑 In this talk, we refer to IIR as such information that is indirectly expressed by ✍️ author / 👨 character / patient / any other entity.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📊 I cover the 1️⃣ pre-processing and 2️⃣ 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": "📊 I cover the 1️⃣ pre-processing and 2️⃣ 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "In pictures below, sharing the quick takeaways on the pipeline construction and experiment results 🧪", "raw": "In pictures below, sharing the quick takeaways on the pipeline construction and experiment results 🧪", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Related paper cards:", "raw": "Related paper cards:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📜 emotion-extraction: ", "raw": "📜 emotion-extraction: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://nicolay-r.github.io/#semeval2024-nicolay", "href": "https://nicolay-r.github.io/#semeval2024-nicolay", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📜 sentiment-analysis: ", "raw": "📜 sentiment-analysis: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://nicolay-r.github.io/#ljom2024", "href": "https://nicolay-r.github.io/#ljom2024", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Models:", "raw": "Models:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/nicolay-r/flan-t5-tsa-thor-base", "href": null, "resource": { "type": "model", "id": "nicolay-r/flan-t5-tsa-thor-base", "discussionNum": null }, "url": "https://huggingface.co/nicolay-r/flan-t5-tsa-thor-base", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base", "href": null, "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", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📓 PS: I got a hoppy for advetising HPMoR ✨ 😁 ", "raw": "📓 PS: I got a hoppy for advetising HPMoR ✨ 😁 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]

📢 For those who are interested in extracting information about ✍️ authors from texts, happy to share personal 📹 on Reading Between the lines: adapting ChatGPT-related systems 🤖 for Implicit Information Retrieval National Youtube: https://youtu.be/nXClX7EDYbE 🔑 In this talk, we refer to IIR as such information that is indirectly expressed by ✍️ author / 👨 character / patient / any other entity. 📊 I cover the 1️⃣ pre-processing and 2️⃣ 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 🧪 Related paper cards: 📜 emotion-extraction: https://nicolay-r.github.io/#semeval2024-nicolay 📜 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 📓 PS: I got a hoppy for advetising HPMoR ✨ 😁

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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, ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "⚡ Optimized performance on Google Cloud's infrastructure, with TGI, TEI, and PyTorch acceleration.", "raw": "⚡ Optimized performance on Google Cloud's infrastructure, with TGI, TEI, and PyTorch acceleration.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🛠️ Hassle-free environment setup, no more dependency issues.", "raw": "🛠️ Hassle-free environment setup, no more dependency issues.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔄 Seamless updates to the latest stable versions.", "raw": "🔄 Seamless updates to the latest stable versions.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💼 Streamlined workflow, reducing dev and maintenance overheads.", "raw": "💼 Streamlined workflow, reducing dev and maintenance overheads.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔒 Robust security features of Google Cloud.", "raw": "🔒 Robust security features of Google Cloud.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "☁️ Fine-tuned for optimal performance, integrated with GKE and Vertex AI.", "raw": "☁️ Fine-tuned for optimal performance, integrated with GKE and Vertex AI.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📦 Community examples for easy experimentation and implementation.", "raw": "📦 Community examples for easy experimentation and implementation.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔜 TPU support for PyTorch Training/Inference and Text Generation Inference is coming soon!", "raw": "🔜 TPU support for PyTorch Training/Inference and Text Generation Inference is coming soon!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Find the documentation at ", "raw": "Find the documentation at ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/docs/google-cloud/en/index", "href": "https://huggingface.co/docs/google-cloud/en/index", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://discuss.huggingface.co/c/google-cloud/69", "href": "https://discuss.huggingface.co/c/google-cloud/69", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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: ⚡ Optimized performance on Google Cloud's infrastructure, with TGI, TEI, and PyTorch acceleration. 🛠️ Hassle-free environment setup, no more dependency issues. 🔄 Seamless updates to the latest stable versions. 💼 Streamlined workflow, reducing dev and maintenance overheads. 🔒 Robust security features of Google Cloud. ☁️ Fine-tuned for optimal performance, integrated with GKE and Vertex AI. 📦 Community examples for easy experimentation and implementation. 🔜 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

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177552598965444

[ { "type": "text", "value": "Hello Hugging Face community!", "raw": "Hello Hugging Face community!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "I wanted to introduce myself and my company ", "raw": "I wanted to introduce myself and my company ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Overlaiapp", "href": null, "resource": null, "url": null, "user": "Overlaiapp", "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- ", "raw": "- ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/Overlaiai/OregonCoastin4K", "href": null, "resource": { "type": "dataset", "id": "Overlaiai/OregonCoastin4K", "discussionNum": null }, "url": "https://huggingface.co/datasets/Overlaiai/OregonCoastin4K", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "- ", "raw": "- ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/Overlaiai/SubArcticPolarBear", "href": null, "resource": { "type": "dataset", "id": "Overlaiai/SubArcticPolarBear", "discussionNum": null }, "url": "https://huggingface.co/datasets/Overlaiai/SubArcticPolarBear", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Overlai.ai Dataset Features", "raw": "Overlai.ai Dataset Features", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🎥 Oversampled: Every clip is captured in stunning 8K resolution, delivering rich detail ideal for fine tuning scenic landscapes and ocean dynamics.", "raw": "🎥 Oversampled: Every clip is captured in stunning 8K resolution, delivering rich detail ideal for fine tuning scenic landscapes and ocean dynamics.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📸 Variance: Includes close-up details, slow-motion footage of crashing waves, sweeping landscapes, and wildlife shots.", "raw": "📸 Variance: Includes close-up details, slow-motion footage of crashing waves, sweeping landscapes, and wildlife shots.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📋 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": "📋 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "⚙️ 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": "⚙️ 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🌅 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": "🌅 Light: Shot during early morning and sunset light for optimal color contrast and dynamic range, maximizing visual quality for color and lighting-sensitive tasks.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔍 Curation: Curated specifically for machine learning, providing clean, high-quality data for next generation model training.", "raw": "🔍 Curation: Curated specifically for machine learning, providing clean, high-quality data for next generation model training.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 🎥 Oversampled: Every clip is captured in stunning 8K resolution, delivering rich detail ideal for fine tuning scenic landscapes and ocean dynamics. 📸 Variance: Includes close-up details, slow-motion footage of crashing waves, sweeping landscapes, and wildlife shots. 📋 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. ⚙️ 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. 🌅 Light: Shot during early morning and sunset light for optimal color contrast and dynamic range, maximizing visual quality for color and lighting-sensitive tasks. 🔍 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

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"new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "->Flux LoRA Collection: ", "raw": "->Flux LoRA Collection: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be", "href": null, "resource": { "type": "collection", "id": "prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be", "discussionNum": null }, "url": "https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "->Stranger Zones: ", "raw": "->Stranger Zones: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/prithivMLmods/stranger-zone-collections-6737118adcf2cb40d66d0c7e", "href": null, "resource": { "type": "collection", "id": "prithivMLmods/stranger-zone-collections-6737118adcf2cb40d66d0c7e", "discussionNum": null }, "url": "https://huggingface.co/collections/prithivMLmods/stranger-zone-collections-6737118adcf2cb40d66d0c7e", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "->LoRA Spaces: ", "raw": "->LoRA Spaces: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": 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Animaker / Anime Engine : 🤗 Only on🚀: 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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594210081206821

[ { "type": "text", "value": "𝗠𝗲𝘁𝗮 𝘁𝗲𝗮𝗺 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗪𝗮𝘁𝗲𝗿𝗺𝗮𝗿𝗸𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗮𝘁 𝗻𝗼𝘁 𝗲𝗱𝗶𝘁 𝗰𝗮𝗻 𝗯𝗿𝗲𝗮𝗸!🛡️", "raw": "𝗠𝗲𝘁𝗮 𝘁𝗲𝗮𝗺 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗪𝗮𝘁𝗲𝗿𝗺𝗮𝗿𝗸𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗮𝘁 𝗻𝗼𝘁 𝗲𝗱𝗶𝘁 𝗰𝗮𝗻 𝗯𝗿𝗲𝗮𝗸!🛡️", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🤔 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! 🎨 ", "raw": "🤔 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! 🎨 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🎭 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": "🎭 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "⛔ 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": "⛔ 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "A team of researchers at Meta was not happy with this. 😤", "raw": "A team of researchers at Meta was not happy with this. 😤", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🏗️ 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲", "raw": "🏗️ 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "▸ 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": "▸ 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", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "▸ [Only during training] The \"Augmenter\" randomly distorts the image: masks parts, crops, resizes, compresses. It's basically torture at this point.", "raw": "▸ [Only during training] The \"Augmenter\" randomly distorts the image: masks parts, crops, resizes, compresses. It's basically torture at this point.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "▸ 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": "▸ 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Gerat work ", "raw": "Gerat work ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@pierrefdz", "href": null, "resource": null, "url": null, "user": "pierrefdz", "label": null, "code": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@tomsander1998", "href": null, "resource": null, "url": null, "user": "tomsander1998", "label": null, "code": null, "lang": null }, { "type": "text", "value": "!", "raw": "!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Paper here 👉 ", "raw": "Paper here 👉 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2411.07231", "href": null, "resource": { "type": "paper", "id": "2411.07231", "discussionNum": null }, "url": "https://huggingface.co/papers/2411.07231", "user": null, "label": "Watermark Anything with Localized Messages (2411.07231)", "code": null, "lang": null } ]

𝗠𝗲𝘁𝗮 𝘁𝗲𝗮𝗺 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗪𝗮𝘁𝗲𝗿𝗺𝗮𝗿𝗸𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗮𝘁 𝗻𝗼𝘁 𝗲𝗱𝗶𝘁 𝗰𝗮𝗻 𝗯𝗿𝗲𝗮𝗸!🛡️ 🤔 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! 🎨 🎭 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. ⛔ 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. 😤 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! 🏗️ 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ▸ 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 ▸ [Only during training] The "Augmenter" randomly distorts the image: masks parts, crops, resizes, compresses. It's basically torture at this point. ▸ 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 👉 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

246

0

615920565093844

[ { "type": "text", "value": "Added ", "raw": "Added ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@amphion", "href": null, "resource": null, "url": null, "user": "amphion", "label": null, "code": null, "lang": null }, { "type": "text", "value": " MaskGCT & ", "raw": " MaskGCT & ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@hexgrad", "href": null, "resource": null, "url": null, "user": "hexgrad", "label": null, "code": null, "lang": 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. 🤞️🍀️", "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. 🤞️🍀️", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena", "href": null, "resource": { "type": "space", "id": "Pendrokar/TTS-Spaces-Arena", "discussionNum": null }, "url": "https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Svngoku/maskgct-audio-lab", "href": null, "resource": { "type": "space", "id": "Svngoku/maskgct-audio-lab", "discussionNum": null }, "url": "https://huggingface.co/spaces/Svngoku/maskgct-audio-lab", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/hexgrad/Kokoro-TTS", "href": null, "resource": { "type": "space", "id": "hexgrad/Kokoro-TTS", "discussionNum": null }, "url": "https://huggingface.co/spaces/hexgrad/Kokoro-TTS", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "I chose ", "raw": "I chose ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Svngoku", "href": null, "resource": null, "url": null, "user": "Svngoku", "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/amphion/maskgct", "href": null, "resource": { "type": "space", "id": "amphion/maskgct", "discussionNum": null }, "url": "https://huggingface.co/spaces/amphion/maskgct", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Had to remove ", "raw": "Had to remove ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "mention", "value": null, "raw": "@mrfakename", "href": null, "resource": null, "url": null, "user": "mrfakename", "label": null, "code": null, "lang": null }, { "type": "text", "value": " 's MetaVoice-1B Space from the available models as that space has been down for quite some time. 🤕️", "raw": " 's MetaVoice-1B Space from the available models as that space has been down for quite some time. 🤕️", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/mrfakename/MetaVoice-1B-v0.1", "href": null, "resource": { "type": "space", "id": "mrfakename/MetaVoice-1B-v0.1", "discussionNum": null }, "url": "https://huggingface.co/spaces/mrfakename/MetaVoice-1B-v0.1", "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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!", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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. 🤞️🍀️ 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. 🤕️ 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 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`browser-intake-datadoghq.com`", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": "browser-intake-datadoghq.com", "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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": "🎉 Celebrating One Year of #SauerkrautLM with Two Groundbreaking Releases! ", "raw": "🎉 Celebrating One Year of #SauerkrautLM with Two Groundbreaking Releases! ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-SFT", "href": null, "resource": { "type": "model", "id": "VAGOsolutions/SauerkrautLM-v2-14b-SFT", "discussionNum": null }, "url": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-SFT", "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO", "href": null, "resource": { "type": "model", "id": "VAGOsolutions/SauerkrautLM-v2-14b-DPO", "discussionNum": null }, "url": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO", "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔬 Technical Breakthroughs:", "raw": "🔬 Technical Breakthroughs:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Innovative three-phase Fine-Tuning approach", "raw": "💠 Innovative three-phase Fine-Tuning approach", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Two-step Spectrum SFT + one-step Spectrum DPO optimization phase for enhanced performance", "raw": "💠 Two-step Spectrum SFT + one-step Spectrum DPO optimization phase for enhanced performance", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Balance of German and English language capabilities", "raw": "💠 Balance of German and English language capabilities", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Advanced function calling - almost on par with Claude-3.5-Sonnet-20240620", "raw": "💠 Advanced function calling - almost on par with Claude-3.5-Sonnet-20240620", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🇩🇪 German Language Excellence:", "raw": "🇩🇪 German Language Excellence:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Enhance German language understanding and generation (SFT Version > DPO Version)", "raw": "💠 Enhance German language understanding and generation (SFT Version > DPO Version)", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Maintain authentic German linguistic nuances", "raw": "💠 Maintain authentic German linguistic nuances", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Improve cross-lingual capabilities", "raw": "💠 Improve cross-lingual capabilities", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Preserve cultural context awareness", "raw": "💠 Preserve cultural context awareness", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📊 Training Innovation:", "raw": "📊 Training Innovation:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Mathematics-focused content (proprietary classifier-selected)", "raw": "💠 Mathematics-focused content (proprietary classifier-selected)", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 High-quality German training data", "raw": "💠 High-quality German training data", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Specialized function calling datasets", "raw": "💠 Specialized function calling datasets", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "💠 Premium multilingual content", "raw": "💠 Premium multilingual content", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🎁 Community Contribution:", "raw": "🎁 Community Contribution:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "1️⃣ SauerkrautLM-Fermented-GER-DPO: 3,300 high-quality German training samples", "raw": "1️⃣ SauerkrautLM-Fermented-GER-DPO: 3,300 high-quality German training samples", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "2️⃣ SauerkrautLM-Fermented-Irrelevance-GER-DPO: 2,000 specialized samples for optimized function call irrelevance handling", "raw": "2️⃣ SauerkrautLM-Fermented-Irrelevance-GER-DPO: 2,000 specialized samples for optimized function call irrelevance handling", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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! 🚀", "raw": "Thank you to our incredible community and partners who have supported us throughout this journey. Here's to another year of AI innovation! 🚀", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]

🎉 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. 🔬 Technical Breakthroughs: 💠 Innovative three-phase Fine-Tuning approach 💠 Two-step Spectrum SFT + one-step Spectrum DPO optimization phase for enhanced performance 💠 Balance of German and English language capabilities 💠 Advanced function calling - almost on par with Claude-3.5-Sonnet-20240620 🇩🇪 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: 💠 Enhance German language understanding and generation (SFT Version > DPO Version) 💠 Maintain authentic German linguistic nuances 💠 Improve cross-lingual capabilities 💠 Preserve cultural context awareness 📊 Training Innovation: Our three-phase approach targeted specific layer percentages (15%, 20% and 25%) with carefully curated datasets, including: 💠 Mathematics-focused content (proprietary classifier-selected) 💠 High-quality German training data 💠 Specialized function calling datasets 💠 Premium multilingual content 🎁 Community Contribution: We're also releasing two new datasets in a few days: 1️⃣ SauerkrautLM-Fermented-GER-DPO: 3,300 high-quality German training samples 2️⃣ 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! 🚀

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2024-11-04T14:34:23.000Z

2024-11-04T14:35:08.561Z

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/posts/DavidGF/380609696467893

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878599389112746

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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 that’s (surprisingly) meaningful to a human.", "raw": "Let me introduce CaSIL – 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 that’s (surprisingly) meaningful to a human.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "I’ve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. It’s built without agent frameworks—just good ol' Python and math—and it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Here’s a quick peek at the steps:", "raw": "I’ve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. It’s built without agent frameworks—just good ol' Python and math—and it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Here’s a quick peek at the steps:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "✨ How CaSIL Works:", "raw": "✨ How CaSIL Works:", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "Does it work? Yes! And in record time, too. Admittedly, the code is rough—two days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; it’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 rough—two days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; it’s a pure algorithm without complex dependencies, making it easy to integrate into your own LLM setups.", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "🔗 Explore the repo here: ", "raw": "🔗 Explore the repo here: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers", "href": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "📜 Example outputs: ", "raw": "📜 Example outputs: ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers/blob/main/examples.md", "href": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers/blob/main/examples.md", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]

Early Morning Before Work Project: 🌌 Introducing Cascade of Semantically Integrated Layers (CaSIL): A Humorously Over-Engineered Algorithm That Actually… Works 🤷‍♂️ Let me introduce CaSIL – 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 that’s (surprisingly) meaningful to a human. I’ve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. It’s built without agent frameworks—just good ol' Python and math—and it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Here’s a quick peek at the steps: ✨ 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 rough—two days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; it’s a pure algorithm without complex dependencies, making it easy to integrate into your own LLM setups. 🔗 Explore the repo here: https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers 📜 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

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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": "🙋🏻‍♂️hey there folks,", "raw": "🙋🏻‍♂️hey there folks,", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "periodic reminder : if you are experiencing ⚠️500 errors ⚠️ or ⚠️ abnormal ", "raw": "periodic reminder : if you are experiencing ⚠️500 errors ⚠️ or ⚠️ abnormal ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`spaces`", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": "spaces", "lang": null }, { "type": "text", "value": " behavior on load or launch ⚠️", "raw": " behavior on load or launch ⚠️", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "text", "value": "we have a thread 👉🏻 ", "raw": "we have a thread 👉🏻 ", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "link", "value": null, "raw": "https://discord.com/channels/879548962464493619/1295847667515129877", "href": "https://discord.com/channels/879548962464493619/1295847667515129877", "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": 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 🤗🤗🤗", "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 🤗🤗🤗", "href": null, "resource": null, "url": null, "user": null, "label": null, "code": null, "lang": null } ]

🙋🏻‍♂️hey there folks, periodic reminder : if you are experiencing ⚠️500 errors ⚠️ or ⚠️ abnormal `spaces` behavior on load or launch ⚠️ we have a thread 👉🏻 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 🤗🤗🤗

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[]

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2024-11-04T11:41:55.000Z

2024-11-09T02:33:22.761Z

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