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[ { "type": "text", "value": "Since new TTS (Text-to-Speech) systems are coming out what feels like every day, and it's currently hard to compare them, my latest project has focused on doing just that.", "raw": "Since new TTS (Text-to-Speech) systems are coming out what feels like every day, and it's currently hard to compare them, my latest project has focused on doing just that.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I was inspired by the ", "raw": "I was inspired by the ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/TTS-AGI/TTS-Arena", "resource": { "type": "space", "id": "TTS-AGI/TTS-Arena", "discussionNum": null }, "url": "https://huggingface.co/spaces/TTS-AGI/TTS-Arena", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " (definitely check it out if you haven't), which compares recent TTS system using crowdsourced A/B testing.", "raw": " (definitely check it out if you haven't), which compares recent TTS system using crowdsourced A/B testing.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I wanted to see if we can also do a similar evaluation with objective metrics and it's now available here:", "raw": "I wanted to see if we can also do a similar evaluation with objective metrics and it's now available here:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/ttsds/benchmark", "resource": { "type": "space", "id": "ttsds/benchmark", "discussionNum": null }, "url": "https://huggingface.co/spaces/ttsds/benchmark", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Anyone can submit a new TTS model, and I hope this can provide a way to get some information on which areas models perform well or poorly in.", "raw": "Anyone can submit a new TTS model, and I hope this can provide a way to get some information on which areas models perform well or poorly in.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The paper with all the details is available here: ", "raw": "The paper with all the details is available here: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2407.12707", "resource": null, "url": null, "href": "https://arxiv.org/abs/2407.12707", "user": null, "lang": null, "code": null, "label": null } ]
Since new TTS (Text-to-Speech) systems are coming out what feels like every day, and it's currently hard to compare them, my latest project has focused on doing just that. I was inspired by the https://huggingface.co/spaces/TTS-AGI/TTS-Arena (definitely check it out if you haven't), which compares recent TTS system using crowdsourced A/B testing. I wanted to see if we can also do a similar evaluation with objective metrics and it's now available here: https://huggingface.co/spaces/ttsds/benchmark Anyone can submit a new TTS model, and I hope this can provide a way to get some information on which areas models perform well or poorly in. The paper with all the details is available here: https://arxiv.org/abs/2407.12707
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2024-07-18T22:16:51.000Z
2024-07-23T12:06:49.283Z
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[ { "type": "text", "value": "Thrilled to share some AI insights for journalism! ๐Ÿ“Š๐Ÿค–", "raw": "Thrilled to share some AI insights for journalism! ๐Ÿ“Š๐Ÿค–", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Just wrote a guest post on ", "raw": "Just wrote a guest post on ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@ndiakopoulos", "resource": null, "url": null, "href": null, "user": "ndiakopoulos", "lang": null, "code": null, "label": null }, { "type": "text", "value": "'s blog about Hugging Face on Sheets tool.", "raw": "'s blog about Hugging Face on Sheets tool.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Why it matters:", "raw": "Why it matters:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”Œ Brings AI power directly to spreadsheets", "raw": "๐Ÿ”Œ Brings AI power directly to spreadsheets", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ˆ Huge potential for data journalism", "raw": "๐Ÿ“ˆ Huge potential for data journalism", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿšซ๐Ÿ’ป No coding required!", "raw": "๐Ÿšซ๐Ÿ’ป No coding required!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "If you've read Nicholas' \"Automating the News\" (a must-read!), you'll appreciate how this tool fits into the evolving landscape of AI in journalism.", "raw": "If you've read Nicholas' \"Automating the News\" (a must-read!), you'll appreciate how this tool fits into the evolving landscape of AI in journalism.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Read here: ", "raw": "Read here: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://generative-ai-newsroom.com/bringing-open-source-models-to-spreadsheets-c440fc4818b4", "resource": null, "url": null, "href": "https://generative-ai-newsroom.com/bringing-open-source-models-to-spreadsheets-c440fc4818b4", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "#AIJournalism #DataJournalism", "raw": "#AIJournalism #DataJournalism", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Thrilled to share some AI insights for journalism! ๐Ÿ“Š๐Ÿค– Just wrote a guest post on @ndiakopoulos's blog about Hugging Face on Sheets tool. Why it matters: ๐Ÿ”Œ Brings AI power directly to spreadsheets ๐Ÿ“ˆ Huge potential for data journalism ๐Ÿšซ๐Ÿ’ป No coding required! If you've read Nicholas' "Automating the News" (a must-read!), you'll appreciate how this tool fits into the evolving landscape of AI in journalism. Read here: https://generative-ai-newsroom.com/bringing-open-source-models-to-spreadsheets-c440fc4818b4 #AIJournalism #DataJournalism
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2024-07-18T20:07:06.000Z
2024-08-26T16:42:25.197Z
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[ { "type": "text", "value": "๐Ÿ‡ท๐Ÿ‡ธ New Benchmark for Serbian Language ๐Ÿ‡ท๐Ÿ‡ธ", "raw": "๐Ÿ‡ท๐Ÿ‡ธ New Benchmark for Serbian Language ๐Ÿ‡ท๐Ÿ‡ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@DjMel", "resource": null, "url": null, "href": null, "user": "DjMel", "lang": null, "code": null, "label": null }, { "type": "text", "value": " and I recently released a new benchmark for Serbian language that measures General Knowledge of LLMs. We had to parse over 20 years of university entrance exams for University of Belgrade, so the dataset is of high quality.", "raw": " and I recently released a new benchmark for Serbian language that measures General Knowledge of LLMs. We had to parse over 20 years of university entrance exams for University of Belgrade, so the dataset is of high quality.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿฅ‡ OAI models still hold the podium places with a significant gap compared to open-source models", "raw": "๐Ÿฅ‡ OAI models still hold the podium places with a significant gap compared to open-source models", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿค” ", "raw": "๐Ÿค” ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Qwen/Qwen2-7B-Instruct", "resource": { "type": "model", "id": "Qwen/Qwen2-7B-Instruct", "discussionNum": null }, "url": "https://huggingface.co/Qwen/Qwen2-7B-Instruct", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " and ", "raw": " and ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct", "resource": { "type": "model", "id": "VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct", "discussionNum": null }, "url": "https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " models show promising results considering they weren't trained on Serbian language", "raw": " models show promising results considering they weren't trained on Serbian language", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ˆ Best open-source model seems to be ", "raw": "๐Ÿ“ˆ Best open-source model seems to be ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Stopwolf/Mustra-7B-Instruct-v0.2", "resource": { "type": "model", "id": "Stopwolf/Mustra-7B-Instruct-v0.2", "discussionNum": null }, "url": "https://huggingface.co/Stopwolf/Mustra-7B-Instruct-v0.2", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ", a merge between ", "raw": ", a merge between ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/gordicaleksa/YugoGPT", "resource": { "type": "model", "id": "gordicaleksa/YugoGPT", "discussionNum": null }, "url": "https://huggingface.co/gordicaleksa/YugoGPT", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " and ", "raw": " and ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2", "resource": { "type": "model", "id": "mistralai/Mistral-7B-Instruct-v0.2", "discussionNum": null }, "url": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“‰ Some models like ", "raw": "๐Ÿ“‰ Some models like ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/google/gemma-2-9b-it", "resource": { "type": "model", "id": "google/gemma-2-9b-it", "discussionNum": null }, "url": "https://huggingface.co/google/gemma-2-9b-it", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " turned out to be a disappointment with random guessing-like accuracy", "raw": " turned out to be a disappointment with random guessing-like accuracy", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Take a look at the whole results at the dataset page:", "raw": "Take a look at the whole results at the dataset page:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/DjMel/oz-eval", "resource": { "type": "dataset", "id": "DjMel/oz-eval", "discussionNum": null }, "url": "https://huggingface.co/datasets/DjMel/oz-eval", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "P.S. If you have any constructive criticism or ideas for improvement, feel free to use dataset's Discussions page!", "raw": "P.S. If you have any constructive criticism or ideas for improvement, feel free to use dataset's Discussions page!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿ‡ท๐Ÿ‡ธ New Benchmark for Serbian Language ๐Ÿ‡ท๐Ÿ‡ธ @DjMel and I recently released a new benchmark for Serbian language that measures General Knowledge of LLMs. We had to parse over 20 years of university entrance exams for University of Belgrade, so the dataset is of high quality. ๐Ÿฅ‡ OAI models still hold the podium places with a significant gap compared to open-source models ๐Ÿค” https://huggingface.co/Qwen/Qwen2-7B-Instruct and https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct models show promising results considering they weren't trained on Serbian language ๐Ÿ“ˆ Best open-source model seems to be https://huggingface.co/Stopwolf/Mustra-7B-Instruct-v0.2, a merge between https://huggingface.co/gordicaleksa/YugoGPT and https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2 ๐Ÿ“‰ Some models like https://huggingface.co/google/gemma-2-9b-it turned out to be a disappointment with random guessing-like accuracy Take a look at the whole results at the dataset page: https://huggingface.co/datasets/DjMel/oz-eval P.S. If you have any constructive criticism or ideas for improvement, feel free to use dataset's Discussions page!
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2024-07-18T17:52:12.000Z
2024-07-18T17:52:12.855Z
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/posts/Stopwolf/685609706603289
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This is the week of small AI language models!
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2024-07-18T15:44:06.000Z
2024-07-20T15:36:06.401Z
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/posts/clem/530713412165384
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[ { "type": "text", "value": "๐Ÿš€ Just released version 0.24.0 of the ๐š‘๐šž๐š๐š๐š’๐š—๐š๐š๐šŠ๐šŒ๐šŽ_๐š‘๐šž๐š‹ Python library!", "raw": "๐Ÿš€ Just released version 0.24.0 of the ๐š‘๐šž๐š๐š๐š’๐š—๐š๐š๐šŠ๐šŒ๐šŽ_๐š‘๐šž๐š‹ Python library!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Exciting updates include:", "raw": "Exciting updates include:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โšก InferenceClient is now a drop-in replacement for OpenAI's chat completion!", "raw": "โšก InferenceClient is now a drop-in replacement for OpenAI's chat completion!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœจ Support for response_format, adapter_id , truncate, and more in InferenceClient ", "raw": "โœจ Support for response_format, adapter_id , truncate, and more in InferenceClient ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’พ Serialization module with a save_torch_model helper that handles shared layers, sharding, naming convention, and safe serialization. Basically a condensed version of logic scattered across safetensors, transformers , accelerate", "raw": "๐Ÿ’พ Serialization module with a save_torch_model helper that handles shared layers, sharding, naming convention, and safe serialization. Basically a condensed version of logic scattered across safetensors, transformers , accelerate", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ Optimized HfFileSystem to avoid getting rate limited when browsing ", "raw": "๐Ÿ“ Optimized HfFileSystem to avoid getting rate limited when browsing ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/HuggingFaceFW/fineweb", "resource": { "type": "dataset", "id": "HuggingFaceFW/fineweb", "discussionNum": null }, "url": "https://huggingface.co/datasets/HuggingFaceFW/fineweb", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”จ HfApi & CLI improvements: prevent empty commits, create repo inside resource group, webhooks API, more options in the Search API, etc.", "raw": "๐Ÿ”จ HfApi & CLI improvements: prevent empty commits, create repo inside resource group, webhooks API, more options in the Search API, etc.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Check out the full release notes for more details: ", "raw": "Check out the full release notes for more details: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Wauplin/huggingface_hub/discussions/7", "resource": { "type": "space", "id": "Wauplin/huggingface_hub", "discussionNum": 7 }, "url": "https://huggingface.co/spaces/Wauplin/huggingface_hub/discussions/7", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ๐Ÿ‘€", "raw": " ๐Ÿ‘€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿš€ Just released version 0.24.0 of the ๐š‘๐šž๐š๐š๐š’๐š—๐š๐š๐šŠ๐šŒ๐šŽ_๐š‘๐šž๐š‹ Python library! Exciting updates include: โšก InferenceClient is now a drop-in replacement for OpenAI's chat completion! โœจ Support for response_format, adapter_id , truncate, and more in InferenceClient ๐Ÿ’พ Serialization module with a save_torch_model helper that handles shared layers, sharding, naming convention, and safe serialization. Basically a condensed version of logic scattered across safetensors, transformers , accelerate ๐Ÿ“ Optimized HfFileSystem to avoid getting rate limited when browsing https://huggingface.co/datasets/HuggingFaceFW/fineweb ๐Ÿ”จ HfApi & CLI improvements: prevent empty commits, create repo inside resource group, webhooks API, more options in the Search API, etc. Check out the full release notes for more details: https://huggingface.co/spaces/Wauplin/huggingface_hub/discussions/7 ๐Ÿ‘€
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2024-07-18T13:33:40.000Z
2024-07-29T07:47:59.935Z
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/posts/Wauplin/607934507622585
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I have invented a method that is better than Diffusion. A company got a billion dollar valuation yesterday for less than what I am currently giving the world for free. I am starting to suspect that is the issue, I am giving it away for free. I meant it to be a gift to the world, but no one will even look at it. I am changing the licensing soon. It will no longer be free. At the moment, you can view exactly how Swarm Neural Networks can do everything Reverse Diffusion can do, for far less money. It can even make API calls. SNN Image Generator: https://huggingface.co/spaces/TuringsSolutions/SNN-Image-Generator SNN Function Caller (Controlled By TinyLlama): https://huggingface.co/spaces/TuringsSolutions/Qwen-2.0.5B-Swarm-Function-Caller
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2024-07-18T12:40:43.000Z
2024-07-20T16:15:03.586Z
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/posts/TuringsSolutions/575393196953115
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[ { "type": "text", "value": "# Offensive Physical Security Reconnaissance Planning Automation with public facing RTSP streams and Moondream", "raw": "# Offensive Physical Security Reconnaissance Planning Automation with public facing RTSP streams and Moondream", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "After some late night casual hacking about on VLMs for criminal attack vector reconnaissance automaton experiments using Moondream (as usual) based image-text-text with pre defined text prompts that are tuned for extracting weakness or customer identity and monitory based theft physical red team engagement reconnaissance and vector of malicious or criminal activity Working on a space. Thanks again for such a wonderful blessing of super power image-text-to-text model with minimal computational power needed ", "raw": "After some late night casual hacking about on VLMs for criminal attack vector reconnaissance automaton experiments using Moondream (as usual) based image-text-text with pre defined text prompts that are tuned for extracting weakness or customer identity and monitory based theft physical red team engagement reconnaissance and vector of malicious or criminal activity Working on a space. Thanks again for such a wonderful blessing of super power image-text-to-text model with minimal computational power needed ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@vikhyatk", "resource": null, "url": null, "href": null, "user": "vikhyatk", "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I have started actually implementing a custom little tool with both static html space sand python gradio spaces on the go which I shall share as hf spaces when done them. ", "raw": "I have started actually implementing a custom little tool with both static html space sand python gradio spaces on the go which I shall share as hf spaces when done them. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "---", "raw": "---", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/vikhyatk/moondream2", "resource": { "type": "space", "id": "vikhyatk/moondream2", "discussionNum": null }, "url": "https://huggingface.co/spaces/vikhyatk/moondream2", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/vikhyatk/moondream2", "resource": { "type": "model", "id": "vikhyatk/moondream2", "discussionNum": null }, "url": "https://huggingface.co/vikhyatk/moondream2", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
# Offensive Physical Security Reconnaissance Planning Automation with public facing RTSP streams and Moondream After some late night casual hacking about on VLMs for criminal attack vector reconnaissance automaton experiments using Moondream (as usual) based image-text-text with pre defined text prompts that are tuned for extracting weakness or customer identity and monitory based theft physical red team engagement reconnaissance and vector of malicious or criminal activity Working on a space. Thanks again for such a wonderful blessing of super power image-text-to-text model with minimal computational power needed @vikhyatk I have started actually implementing a custom little tool with both static html space sand python gradio spaces on the go which I shall share as hf spaces when done them. --- https://huggingface.co/spaces/vikhyatk/moondream2 https://huggingface.co/vikhyatk/moondream2
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2024-07-18T11:18:57.000Z
2024-07-18T18:37:31.443Z
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/posts/Csplk/926337297827024
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201610890332989
[ { "type": "text", "value": "Excited to share my \"Focus Mode\" Playlist, code name \"The AI/ML Researcher's Playlist\" :)", "raw": "Excited to share my \"Focus Mode\" Playlist, code name \"The AI/ML Researcher's Playlist\" :)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "No lyrics, no beat, just a harmonious sequence of piano melody that will take you places beyond your reasoning/thinking prowess, trust me Iโ€™ve been there lol ๐ŸŽน๐ŸŽถ", "raw": "No lyrics, no beat, just a harmonious sequence of piano melody that will take you places beyond your reasoning/thinking prowess, trust me Iโ€™ve been there lol ๐ŸŽน๐ŸŽถ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Thanks to an amazing pianist and composer on instagram @andreavanzo_composer who played all the songs in this playlist. ", "raw": "Thanks to an amazing pianist and composer on instagram @andreavanzo_composer who played all the songs in this playlist. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Currently have a total of 16 songs, I will keep adding more when I find them.", "raw": "Currently have a total of 16 songs, I will keep adding more when I find them.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Full playlist: ", "raw": "Full playlist: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://youtu.be/2ccxanKmzZY?si=x6weX2AgY5Zpadfw", "resource": null, "url": null, "href": "https://youtu.be/2ccxanKmzZY?si=x6weX2AgY5Zpadfw", "user": null, "lang": null, "code": null, "label": null } ]
Excited to share my "Focus Mode" Playlist, code name "The AI/ML Researcher's Playlist" :) No lyrics, no beat, just a harmonious sequence of piano melody that will take you places beyond your reasoning/thinking prowess, trust me Iโ€™ve been there lol ๐ŸŽน๐ŸŽถ Thanks to an amazing pianist and composer on instagram @andreavanzo_composer who played all the songs in this playlist. Currently have a total of 16 songs, I will keep adding more when I find them. Full playlist: https://youtu.be/2ccxanKmzZY?si=x6weX2AgY5Zpadfw
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2024-07-18T02:04:53.000Z
2024-07-20T02:24:22.121Z
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/posts/Jaward/201610890332989
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[ { "type": "text", "value": "GraphRAG-Ollama-UI", "raw": "GraphRAG-Ollama-UI", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I've been working on a local version of Microsoft's GraphRAG that uses Ollama for everything. It's got a new interactive UI built with Gradio that makes it easier to manage data, run queries, and visualize results. It's not fully featured or set up to harness the entire GraphRAG library yet but it allows you to run all the standard commands for Indexing/Processing and chatting with your graph. Some key features:", "raw": "I've been working on a local version of Microsoft's GraphRAG that uses Ollama for everything. It's got a new interactive UI built with Gradio that makes it easier to manage data, run queries, and visualize results. It's not fully featured or set up to harness the entire GraphRAG library yet but it allows you to run all the standard commands for Indexing/Processing and chatting with your graph. Some key features:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Uses local models via Ollama for LLM and embeddings", "raw": "Uses local models via Ollama for LLM and embeddings", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "3D graph visualization of the knowledge graph using Plotly", "raw": "3D graph visualization of the knowledge graph using Plotly", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "File management through the UI (upload, view, edit, delete)", "raw": "File management through the UI (upload, view, edit, delete)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Settings management in the interface", "raw": "Settings management in the interface", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Real-time logging for debugging", "raw": "Real-time logging for debugging", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/severian42/GraphRAG-Ollama-UI", "resource": null, "url": null, "href": "https://github.com/severian42/GraphRAG-Ollama-UI", "user": null, "lang": null, "code": null, "label": null } ]
GraphRAG-Ollama-UI I've been working on a local version of Microsoft's GraphRAG that uses Ollama for everything. It's got a new interactive UI built with Gradio that makes it easier to manage data, run queries, and visualize results. It's not fully featured or set up to harness the entire GraphRAG library yet but it allows you to run all the standard commands for Indexing/Processing and chatting with your graph. Some key features: Uses local models via Ollama for LLM and embeddings 3D graph visualization of the knowledge graph using Plotly File management through the UI (upload, view, edit, delete) Settings management in the interface Real-time logging for debugging https://github.com/severian42/GraphRAG-Ollama-UI
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2024-07-17T17:46:09.000Z
2024-07-18T05:28:18.167Z
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/posts/Severian/955430113136205
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[ { "type": "text", "value": "#ICLM 2024 is almost there ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ PM if you will be in Vienna next week, Glad to catchup with the Hugging Face community there!", "raw": "#ICLM 2024 is almost there ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ PM if you will be in Vienna next week, Glad to catchup with the Hugging Face community there!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I would like to contribute ๐ŸŽ by releasing the sixth Knowledge Vault, with 100 lectures visualized from the last 10 years of ICML from 2014 to 2024, (10 from 2024 will be included after the conference) including knowledge graphs for all the Invited Lectures and some extras, with almost 3000 topics represented using AI.", "raw": "I would like to contribute ๐ŸŽ by releasing the sixth Knowledge Vault, with 100 lectures visualized from the last 10 years of ICML from 2014 to 2024, (10 from 2024 will be included after the conference) including knowledge graphs for all the Invited Lectures and some extras, with almost 3000 topics represented using AI.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "You can explore it here:", "raw": "You can explore it here:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒ ", "raw": "๐ŸŒ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://theendofknowledge.com/Vaults/6/ICML-2015-2024.html", "resource": null, "url": null, "href": "https://theendofknowledge.com/Vaults/6/ICML-2015-2024.html", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "And you can learn more about the Vaults here:", "raw": "And you can learn more about the Vaults here:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“https://www.linkedin.com/pulse/knowledge-vaults-david-vivancos-lbjef/", "raw": "๐Ÿ“https://www.linkedin.com/pulse/knowledge-vaults-david-vivancos-lbjef/", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "And previous Vaults relevant to the #huggingface community are:", "raw": "And previous Vaults relevant to the #huggingface community are:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒ [ ", "raw": "๐ŸŒ [ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@lexfridman", "resource": null, "url": null, "href": null, "user": "lexfridman", "lang": null, "code": null, "label": null }, { "type": "text", "value": " 2018-2024 Interviews] ", "raw": " 2018-2024 Interviews] ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://theendofknowledge.com/Vaults/1/Lex100-2024.html", "resource": null, "url": null, "href": "https://theendofknowledge.com/Vaults/1/Lex100-2024.html", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒ [ICLR 2014-2023] ", "raw": "๐ŸŒ [ICLR 2014-2023] ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://theendofknowledge.com/Vaults/2/ICLR2014-2023.html", "resource": null, "url": null, "href": "https://theendofknowledge.com/Vaults/2/ICLR2014-2023.html", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒ [AIForGood 2017-2024] ", "raw": "๐ŸŒ [AIForGood 2017-2024] ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://theendofknowledge.com/Vaults/4/AIForGood2017-2024.html", "resource": null, "url": null, "href": "https://theendofknowledge.com/Vaults/4/AIForGood2017-2024.html", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒ [CVPR 2015-2024] ", "raw": "๐ŸŒ [CVPR 2015-2024] ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://theendofknowledge.com/Vaults/5/CVPR-2015-2024.html", "resource": null, "url": null, "href": "https://theendofknowledge.com/Vaults/5/CVPR-2015-2024.html", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Hope you like them!", "raw": "Hope you like them!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "And great to see you all at #icml2024 ", "raw": "And great to see you all at #icml2024 ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@clem", "resource": null, "url": null, "href": null, "user": "clem", "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@thomwolf", "resource": null, "url": null, "href": null, "user": "thomwolf", "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@julien-c", "resource": null, "url": null, "href": null, "user": "julien-c", "lang": null, "code": null, "label": null }, { "type": "text", "value": " and team", "raw": " and team", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
#ICLM 2024 is almost there ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ PM if you will be in Vienna next week, Glad to catchup with the Hugging Face community there! I would like to contribute ๐ŸŽ by releasing the sixth Knowledge Vault, with 100 lectures visualized from the last 10 years of ICML from 2014 to 2024, (10 from 2024 will be included after the conference) including knowledge graphs for all the Invited Lectures and some extras, with almost 3000 topics represented using AI. You can explore it here: ๐ŸŒ https://theendofknowledge.com/Vaults/6/ICML-2015-2024.html And you can learn more about the Vaults here: ๐Ÿ“https://www.linkedin.com/pulse/knowledge-vaults-david-vivancos-lbjef/ And previous Vaults relevant to the #huggingface community are: ๐ŸŒ [ @lexfridman 2018-2024 Interviews] https://theendofknowledge.com/Vaults/1/Lex100-2024.html ๐ŸŒ [ICLR 2014-2023] https://theendofknowledge.com/Vaults/2/ICLR2014-2023.html ๐ŸŒ [AIForGood 2017-2024] https://theendofknowledge.com/Vaults/4/AIForGood2017-2024.html ๐ŸŒ [CVPR 2015-2024] https://theendofknowledge.com/Vaults/5/CVPR-2015-2024.html Hope you like them! And great to see you all at #icml2024 @clem @thomwolf @julien-c and team
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2024-07-17T15:20:02.000Z
2024-07-17T15:20:02.294Z
[]
/posts/DavidVivancos/800610384384427
1,667
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[ { "type": "text", "value": "Using the new viewer iframe support for the datasets viewer, I built a simple Space ", "raw": "Using the new viewer iframe support for the datasets viewer, I built a simple Space ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/davanstrien/collection_dataset_viewer", "resource": { "type": "space", "id": "davanstrien/collection_dataset_viewer", "discussionNum": null }, "url": "https://huggingface.co/spaces/davanstrien/collection_dataset_viewer", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " to quickly explore all the datasets inside a collection. ", "raw": " to quickly explore all the datasets inside a collection. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The collection is loaded from an environment variable, so you can duplicate this Space to create a Space for exploring datasets in another collection! ", "raw": "The collection is loaded from an environment variable, so you can duplicate this Space to create a Space for exploring datasets in another collection! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Using the new viewer iframe support for the datasets viewer, I built a simple Space https://huggingface.co/spaces/davanstrien/collection_dataset_viewer to quickly explore all the datasets inside a collection. The collection is loaded from an environment variable, so you can duplicate this Space to create a Space for exploring datasets in another collection!
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2024-07-17T14:37:35.000Z
2024-07-17T14:37:35.190Z
[]
/posts/davanstrien/959637893605388
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[ { "type": "text", "value": "palmer-004 becomes ๐Ÿ”ฅturbo๐Ÿ”ฅ now is half the size, twice the speed and the best overall 0.5b language model in huggingface.", "raw": "palmer-004 becomes ๐Ÿ”ฅturbo๐Ÿ”ฅ now is half the size, twice the speed and the best overall 0.5b language model in huggingface.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/appvoid/palmer-004-turbo", "resource": { "type": "model", "id": "appvoid/palmer-004-turbo", "discussionNum": null }, "url": "https://huggingface.co/appvoid/palmer-004-turbo", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
palmer-004 becomes ๐Ÿ”ฅturbo๐Ÿ”ฅ now is half the size, twice the speed and the best overall 0.5b language model in huggingface. https://huggingface.co/appvoid/palmer-004-turbo
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2024-07-17T12:15:08.000Z
2024-07-17T19:30:38.435Z
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/posts/appvoid/665227462229685
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[ { "type": "text", "value": "๐Ÿคฏ Ghost 8B Beta emerges as a clear leader, surpassing even proprietary models like xAI Grok 1, OpenAI GPT 3.5, and Mistral Mixtral 8x7B. This dominance extends to its parity with Mistral Medium, further solidifying its position as a top-tier language model. Furthermore, Ghost 8B Beta stands out as one of only three models employing the zero-shot method for evaluation, alongside Claude 2 and Claude 3, showcasing its unique capabilities and potential for groundbreaking applications. ", "raw": "๐Ÿคฏ Ghost 8B Beta emerges as a clear leader, surpassing even proprietary models like xAI Grok 1, OpenAI GPT 3.5, and Mistral Mixtral 8x7B. This dominance extends to its parity with Mistral Medium, further solidifying its position as a top-tier language model. Furthermore, Ghost 8B Beta stands out as one of only three models employing the zero-shot method for evaluation, alongside Claude 2 and Claude 3, showcasing its unique capabilities and potential for groundbreaking applications. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "--- ", "raw": "--- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’ฌ Chat with the model here: ", "raw": "๐Ÿ’ฌ Chat with the model here: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Playground with Ghost 8B Beta (ฮฒ, 8k): ", "raw": "- Playground with Ghost 8B Beta (ฮฒ, 8k): ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k", "resource": { "type": "space", "id": "lamhieu/ghost-8b-beta-8k", "discussionNum": null }, "url": "https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Playground with Ghost 8B Beta (ฮฒ, 128k): ", "raw": "- Playground with Ghost 8B Beta (ฮฒ, 128k): ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/lamhieu/ghost-8b-beta-128k", "resource": { "type": "space", "id": "lamhieu/ghost-8b-beta-128k", "discussionNum": null }, "url": "https://huggingface.co/spaces/lamhieu/ghost-8b-beta-128k", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Official website: ", "raw": "- Official website: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://ghost-x.org/docs/models/ghost-8b-beta/", "resource": null, "url": null, "href": "https://ghost-x.org/docs/models/ghost-8b-beta/", "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿคฏ Ghost 8B Beta emerges as a clear leader, surpassing even proprietary models like xAI Grok 1, OpenAI GPT 3.5, and Mistral Mixtral 8x7B. This dominance extends to its parity with Mistral Medium, further solidifying its position as a top-tier language model. Furthermore, Ghost 8B Beta stands out as one of only three models employing the zero-shot method for evaluation, alongside Claude 2 and Claude 3, showcasing its unique capabilities and potential for groundbreaking applications. --- ๐Ÿ’ฌ Chat with the model here: - Playground with Ghost 8B Beta (ฮฒ, 8k): https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k - Playground with Ghost 8B Beta (ฮฒ, 128k): https://huggingface.co/spaces/lamhieu/ghost-8b-beta-128k - Official website: https://ghost-x.org/docs/models/ghost-8b-beta/
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2024-07-17T08:18:35.000Z
2024-07-22T17:44:48.436Z
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/posts/lamhieu/547009284011325
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No on really pays attention to voyage ai , but probably you should ๐Ÿ˜‰ https://huggingface.co/voyageai
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2024-07-17T07:11:11.000Z
2024-07-19T01:51:45.834Z
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/posts/Tonic/787664814277596
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434296828609012
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Decensored Gemma2-27b https://huggingface.co/TheDrummer/Big-Tiger-Gemma-27B-v1
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2024-07-17T04:54:23.000Z
2024-07-17T04:54:23.973Z
[]
/posts/KnutJaegersberg/434296828609012
1,363
0
793020227673362
[ { "type": "text", "value": "Micrograd in pure C๐Ÿค•", "raw": "Micrograd in pure C๐Ÿค•", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Port of Karpathy's micrograd in pure C. ", "raw": "Port of Karpathy's micrograd in pure C. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Yo C does not negotiate with memory ๐Ÿ˜‚", "raw": "Yo C does not negotiate with memory ๐Ÿ˜‚", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Code: ", "raw": "Code: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/Jaykef/micrograd.c", "resource": null, "url": null, "href": "https://github.com/Jaykef/micrograd.c", "user": null, "lang": null, "code": null, "label": null } ]
Micrograd in pure C๐Ÿค• Port of Karpathy's micrograd in pure C. Yo C does not negotiate with memory ๐Ÿ˜‚ Code: https://github.com/Jaykef/micrograd.c
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2024-07-17T00:34:20.000Z
2024-07-19T16:42:52.311Z
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/posts/Jaward/793020227673362
1,643
2
122117725261447
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I've made a creative version of Tile Upscaler - https://huggingface.co/spaces/gokaygokay/TileUpscalerV2 - https://github.com/gokayfem/Tile-Upscaler - New tiling strategy - Now it's closer to Clarity Upscaler - It has more parameters to play and it has more room to fail because of that - You should try different resolutions, strength and controlnet strength Original Tile Upscaler - https://huggingface.co/spaces/gokaygokay/Tile-Upscaler
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2024-07-16T22:56:48.000Z
2024-07-17T18:35:48.062Z
[]
/posts/gokaygokay/122117725261447
4,630
0
328638870427201
[ { "type": "text", "value": "What an eventful day in Open Source LLMs today:", "raw": "What an eventful day in Open Source LLMs today:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Mistral released Codestral Mamba ๐Ÿ", "raw": "Mistral released Codestral Mamba ๐Ÿ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Beats DeepSeek QwenCode, best model < 10B, competitive with Codestral 22B", "raw": "> Beats DeepSeek QwenCode, best model < 10B, competitive with Codestral 22B", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Mamba 2 architecture - supports up to 256K context", "raw": "> Mamba 2 architecture - supports up to 256K context", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Apache 2.0 licensed, perfect for local code assistant", "raw": "> Apache 2.0 licensed, perfect for local code assistant", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Transformers & llama.cpp integration upcoming!", "raw": "> Transformers & llama.cpp integration upcoming!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Model checkpoint: ", "raw": "Model checkpoint: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/mistralai/mamba-codestral-7B-v0.1", "resource": null, "url": null, "href": "https://huggingface.co/mistralai/mamba-codestral-7B-v0.1", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Hugging Face dropped SmolLM ๐Ÿค", "raw": "Hugging Face dropped SmolLM ๐Ÿค", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Beats MobileLLM, Qwen 0.5B, Phi 1.5B and more!", "raw": "> Beats MobileLLM, Qwen 0.5B, Phi 1.5B and more!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> 135M, 360M, and 1.7B param model checkpoints", "raw": "> 135M, 360M, and 1.7B param model checkpoints", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Trained on 600B high-quality synthetic + FineWeb Edu tokens", "raw": "> Trained on 600B high-quality synthetic + FineWeb Edu tokens", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Architecture: Llama + GQA + 2048 ctx length ", "raw": "> Architecture: Llama + GQA + 2048 ctx length ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Ripe for fine-tuning and on-device deployments.", "raw": "> Ripe for fine-tuning and on-device deployments.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Works out of the box with Transformers!", "raw": "> Works out of the box with Transformers!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Model checkpoints: ", "raw": "Model checkpoints: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966", "resource": { "type": "collection", "id": "HuggingFaceTB/smollm-6695016cad7167254ce15966", "discussionNum": null }, "url": "https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Mistral released Mathstral 7B โˆ‘", "raw": "Mistral released Mathstral 7B โˆ‘", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> 56.6% on MATH and 63.47% on MMLU", "raw": "> 56.6% on MATH and 63.47% on MMLU", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Same architecture as Mistral 7B", "raw": "> Same architecture as Mistral 7B", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Works out of the box with Transformers & llama.cpp ", "raw": "> Works out of the box with Transformers & llama.cpp ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "> Released under Apache 2.0 license", "raw": "> Released under Apache 2.0 license", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Model checkpoint: ", "raw": "Model checkpoint: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/mistralai/mathstral-7B-v0.1", "resource": null, "url": null, "href": "https://huggingface.co/mistralai/mathstral-7B-v0.1", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Pretty dope day for open source ML. Can't wait to see what the community builds with it and to support them further! ๐Ÿค—", "raw": "Pretty dope day for open source ML. Can't wait to see what the community builds with it and to support them further! ๐Ÿค—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "What's your favourite from the release today?", "raw": "What's your favourite from the release today?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
What an eventful day in Open Source LLMs today: Mistral released Codestral Mamba ๐Ÿ > Beats DeepSeek QwenCode, best model < 10B, competitive with Codestral 22B > Mamba 2 architecture - supports up to 256K context > Apache 2.0 licensed, perfect for local code assistant > Transformers & llama.cpp integration upcoming! Model checkpoint: https://huggingface.co/mistralai/mamba-codestral-7B-v0.1 Hugging Face dropped SmolLM ๐Ÿค > Beats MobileLLM, Qwen 0.5B, Phi 1.5B and more! > 135M, 360M, and 1.7B param model checkpoints > Trained on 600B high-quality synthetic + FineWeb Edu tokens > Architecture: Llama + GQA + 2048 ctx length > Ripe for fine-tuning and on-device deployments. > Works out of the box with Transformers! Model checkpoints: https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966 Mistral released Mathstral 7B โˆ‘ > 56.6% on MATH and 63.47% on MMLU > Same architecture as Mistral 7B > Works out of the box with Transformers & llama.cpp > Released under Apache 2.0 license Model checkpoint: https://huggingface.co/mistralai/mathstral-7B-v0.1 Pretty dope day for open source ML. Can't wait to see what the community builds with it and to support them further! ๐Ÿค— What's your favourite from the release today?
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2024-07-16T22:36:54.000Z
2024-07-17T11:21:21.048Z
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/posts/reach-vb/328638870427201
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What could have been "{{bos_token}}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '<|user|>\n' + message['content'] + '<|end|>\n' }}{% elif message['role'] == 'assistant' %}{{ '<|bot|>\n' + message['content'] }}{% if loop.last %}{{ '<|end|>' + eos_token }}{% else %}{{ '<|end|>\n' }}{% endif %}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
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2024-07-16T21:25:33.000Z
2024-07-16T22:13:36.000Z
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/posts/nroggendorff/875200491029475
552
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[ { "type": "text", "value": "Small models, BIG impact: SmolLM is here! ๐Ÿš€๐Ÿ”ฌ", "raw": "Small models, BIG impact: SmolLM is here! ๐Ÿš€๐Ÿ”ฌ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We're launching a series of small but mighty language models:", "raw": "We're launching a series of small but mighty language models:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŽ๏ธ Super fast - runs on laptops, phones, you name it!", "raw": "๐ŸŽ๏ธ Super fast - runs on laptops, phones, you name it!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ 3 sizes: 130M, 350M, and 1.5B parameters", "raw": "๐Ÿ“ 3 sizes: 130M, 350M, and 1.5B parameters", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿฅ‡ Outperforms same size models from Meta, Microsoft, and Qwen", "raw": "๐Ÿฅ‡ Outperforms same size models from Meta, Microsoft, and Qwen", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”“ Fully open-source: datasets, training code, models", "raw": "๐Ÿ”“ Fully open-source: datasets, training code, models", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Š๐ž๐ฒ ๐Ÿ๐ž๐š๐ญ๐ฎ๐ซ๐ž๐ฌ", "raw": "๐Š๐ž๐ฒ ๐Ÿ๐ž๐š๐ญ๐ฎ๐ซ๐ž๐ฌ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Trained on FineWeb-Edu and Cosmopedia v2 (largest synthetic pre-training dataset)", "raw": "- Trained on FineWeb-Edu and Cosmopedia v2 (largest synthetic pre-training dataset)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- No cloud needed - run locally for privacy and energy efficiency", "raw": "- No cloud needed - run locally for privacy and energy efficiency", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Everything is public, from data curation to training steps", "raw": "- Everything is public, from data curation to training steps", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐๐จ๐ญ๐ž๐ง๐ญ๐ข๐š๐ฅ ๐ฎ๐ฌ๐ž ๐œ๐š๐ฌ๐ž๐ฌ", "raw": "๐๐จ๐ญ๐ž๐ง๐ญ๐ข๐š๐ฅ ๐ฎ๐ฌ๐ž ๐œ๐š๐ฌ๐ž๐ฌ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- On-device autocomplete", "raw": "- On-device autocomplete", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Local request parsing", "raw": "- Local request parsing", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Custom fine-tuning for specific needs without the need for expensive GPUs", "raw": "- Custom fine-tuning for specific needs without the need for expensive GPUs", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐†๐จ ๐๐ž๐ž๐ฉ๐ž๐ซ", "raw": "๐†๐จ ๐๐ž๐ž๐ฉ๐ž๐ซ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰ Check it out: ", "raw": "๐Ÿ‘‰ Check it out: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collections/HuggingFaceTB/smollm-models-6695016cad7167254ce15966", "resource": null, "url": null, "href": "https://huggingface.co/collections/HuggingFaceTB/smollm-models-6695016cad7167254ce15966", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰ Run the 360M model in your browser, 100 % private: ", "raw": "๐Ÿ‘‰ Run the 360M model in your browser, 100 % private: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/HuggingFaceTB/SmolLM-360M-Instruct-WebGPU", "resource": { "type": "space", "id": "HuggingFaceTB/SmolLM-360M-Instruct-WebGPU", "discussionNum": null }, "url": "https://huggingface.co/spaces/HuggingFaceTB/SmolLM-360M-Instruct-WebGPU", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰ Read the blog explaining everything in detail: huggingface.co/blog/smollm", "raw": "๐Ÿ‘‰ Read the blog explaining everything in detail: huggingface.co/blog/smollm", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Kudos to the stellar team who worked on this project: ", "raw": "Kudos to the stellar team who worked on this project: ", "resource": 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Small models, BIG impact: SmolLM is here! ๐Ÿš€๐Ÿ”ฌ We're launching a series of small but mighty language models: ๐ŸŽ๏ธ Super fast - runs on laptops, phones, you name it! ๐Ÿ“ 3 sizes: 130M, 350M, and 1.5B parameters ๐Ÿฅ‡ Outperforms same size models from Meta, Microsoft, and Qwen ๐Ÿ”“ Fully open-source: datasets, training code, models ๐Š๐ž๐ฒ ๐Ÿ๐ž๐š๐ญ๐ฎ๐ซ๐ž๐ฌ - Trained on FineWeb-Edu and Cosmopedia v2 (largest synthetic pre-training dataset) - No cloud needed - run locally for privacy and energy efficiency - Everything is public, from data curation to training steps ๐๐จ๐ญ๐ž๐ง๐ญ๐ข๐š๐ฅ ๐ฎ๐ฌ๐ž ๐œ๐š๐ฌ๐ž๐ฌ - On-device autocomplete - Local request parsing - Custom fine-tuning for specific needs without the need for expensive GPUs ๐†๐จ ๐๐ž๐ž๐ฉ๐ž๐ซ ๐Ÿ‘‰ Check it out: https://huggingface.co/collections/HuggingFaceTB/smollm-models-6695016cad7167254ce15966 ๐Ÿ‘‰ Run the 360M model in your browser, 100 % private: https://huggingface.co/spaces/HuggingFaceTB/SmolLM-360M-Instruct-WebGPU ๐Ÿ‘‰ Read the blog explaining everything in detail: huggingface.co/blog/smollm Kudos to the stellar team who worked on this project: @loubnabnl @anton-l @eliebak @lvwerra
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2024-07-16T17:39:37.000Z
2024-07-16T17:39:37.821Z
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[ { "type": "text", "value": "Cool things this week from ", "raw": "Cool things this week from ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@huggingface", "resource": null, "url": null, "href": null, "user": "huggingface", "lang": null, "code": null, "label": null }, { "type": "text", "value": "!", "raw": "!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸŒŽAI math olympiad winner NuminaMath is here!", "raw": "๐ŸŒŽAI math olympiad winner NuminaMath is here!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿค—Announcing New Hugging Face and Keras NLP integration", "raw": "๐Ÿค—Announcing New Hugging Face and Keras NLP integration", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœจUI overhaul to HF tokens! ", "raw": "โœจUI overhaul to HF tokens! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐ŸงŠ Embed our dataset viewer on any webpage!", "raw": "๐ŸงŠ Embed our dataset viewer on any webpage!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/winning-aimo-progress-prize", "resource": null, "url": null, "href": "https://huggingface.co/blog/winning-aimo-progress-prize", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/keras-nlp-integration", "resource": null, "url": null, "href": "https://huggingface.co/blog/keras-nlp-integration", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/settings/tokens", "resource": null, "url": null, "href": "https://huggingface.co/settings/tokens", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://x.com/julien_c/status/1812099420726456457", "resource": null, "url": null, "href": "https://x.com/julien_c/status/1812099420726456457", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Check out the full list on our discord! ๐Ÿ‘‡", "raw": "Check out the full list on our discord! ๐Ÿ‘‡", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://discord.com/invite/JfAtkvEtRb", "resource": null, "url": null, "href": "https://discord.com/invite/JfAtkvEtRb", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Cool things this week from @huggingface! ๐ŸŒŽAI math olympiad winner NuminaMath is here! ๐Ÿค—Announcing New Hugging Face and Keras NLP integration โœจUI overhaul to HF tokens! ๐ŸงŠ Embed our dataset viewer on any webpage! https://huggingface.co/blog/winning-aimo-progress-prize https://huggingface.co/blog/keras-nlp-integration https://huggingface.co/settings/tokens https://x.com/julien_c/status/1812099420726456457 Check out the full list on our discord! ๐Ÿ‘‡ https://discord.com/invite/JfAtkvEtRb
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2024-07-16T15:22:26.000Z
2024-07-16T15:22:26.679Z
[]
/posts/lunarflu/618460361384960
1,815
0
799720029968177
[ { "type": "text", "value": "The community Journalists on HuggingFace recently launched a tool (", "raw": "The community Journalists on HuggingFace recently launched a tool (", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/JournalistsonHF/text-to-image-bias", "resource": { "type": "space", "id": "JournalistsonHF/text-to-image-bias", "discussionNum": null }, "url": "https://huggingface.co/spaces/JournalistsonHF/text-to-image-bias", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ") to compare biases across several text-to-image models. I forked my own to evaluate the SDXL models I made.", "raw": ") to compare biases across several text-to-image models. I forked my own to evaluate the SDXL models I made.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "## SinteticoXL Bias:", "raw": "## SinteticoXL Bias:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/lucianosb/sinteticoXL-bias", "resource": { "type": "space", "id": "lucianosb/sinteticoXL-bias", "discussionNum": null }, "url": "https://huggingface.co/spaces/lucianosb/sinteticoXL-bias", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
The community Journalists on HuggingFace recently launched a tool (https://huggingface.co/spaces/JournalistsonHF/text-to-image-bias) to compare biases across several text-to-image models. I forked my own to evaluate the SDXL models I made. ## SinteticoXL Bias: https://huggingface.co/spaces/lucianosb/sinteticoXL-bias
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2024-07-16T13:21:01.000Z
2024-07-16T17:28:56.217Z
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/posts/lucianosb/799720029968177
1,595
1
737813976382134
[ { "type": "text", "value": "๐Ÿ”ดโญ New addition to the existing concept space! ๐Ÿ”ดโญ", "raw": "๐Ÿ”ดโญ New addition to the existing concept space! ๐Ÿ”ดโญ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿž๏ธ Space: ", "raw": "๐Ÿž๏ธ Space: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/prithivMLmods/IMAGINEO-4K", "resource": { "type": "space", "id": "prithivMLmods/IMAGINEO-4K", "discussionNum": null }, "url": "https://huggingface.co/spaces/prithivMLmods/IMAGINEO-4K", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ Tried the Duotone Canvas with the image generator. Unlike the duotone filter in the Canva app, which applies hue and tints in RGBA, this feature applies duotones based purely on the provided prompt to personalize the generated image.", "raw": "๐Ÿš€ Tried the Duotone Canvas with the image generator. Unlike the duotone filter in the Canva app, which applies hue and tints in RGBA, this feature applies duotones based purely on the provided prompt to personalize the generated image.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ These tones also work with the gridding option, which already exists in the space.", "raw": "๐Ÿš€ These tones also work with the gridding option, which already exists in the space.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ The application of tones depends on the quality and detail of the prompt given. The palette may be distorted in some cases.", "raw": "๐Ÿš€ The application of tones depends on the quality and detail of the prompt given. The palette may be distorted in some cases.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€It doesn't apply like a hue or tint in RGBA (as shown in canva app below); it is purely based on the prompts passed.", "raw": "๐Ÿš€It doesn't apply like a hue or tint in RGBA (as shown in canva app below); it is purely based on the prompts passed.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿž๏ธ Check out the space: ", "raw": "๐Ÿž๏ธ Check out the space: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/prithivMLmods/IMAGINEO-4K", "resource": { "type": "space", "id": "prithivMLmods/IMAGINEO-4K", "discussionNum": null }, "url": "https://huggingface.co/spaces/prithivMLmods/IMAGINEO-4K", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿœ๏ธCollection: ", "raw": "๐Ÿœ๏ธCollection: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collections/prithivMLmods/collection-zero-65e48a7dd8212873836ceca2", "resource": null, "url": null, "href": "https://huggingface.co/collections/prithivMLmods/collection-zero-65e48a7dd8212873836ceca2", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "code_fence", "value": null, "raw": "```\ngit.lnyan.com/spaces/prithivMLmods/IMAGINEO-4K\n```", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": "huggingface.co/spaces/prithivMLmods/IMAGINEO-4K", "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿž๏ธWhat you can do with this space:", "raw": "๐Ÿž๏ธWhat you can do with this space:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Compose Image Grid", "raw": "โœ… Compose Image Grid", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰๐Ÿป \"2x1\", \"1x2\", \"2x2\", \"2x3\", \"3x2\", \"1x1\"", "raw": "๐Ÿ‘‰๐Ÿป \"2x1\", \"1x2\", \"2x2\", \"2x3\", \"3x2\", \"1x1\"", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Apply styles", "raw": "โœ… Apply styles", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Set up Image tones", "raw": "โœ… Set up Image tones", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Apply filters & adjust quality", "raw": "โœ… Apply filters & adjust quality", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Thanks for reading!", "raw": "Thanks for reading!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@prithivMLmods", "resource": null, "url": null, "href": null, "user": "prithivMLmods", "lang": null, "code": null, "label": null } ]
๐Ÿ”ดโญ New addition to the existing concept space! ๐Ÿ”ดโญ ๐Ÿž๏ธ Space: https://huggingface.co/spaces/prithivMLmods/IMAGINEO-4K ๐Ÿš€ Tried the Duotone Canvas with the image generator. Unlike the duotone filter in the Canva app, which applies hue and tints in RGBA, this feature applies duotones based purely on the provided prompt to personalize the generated image. ๐Ÿš€ These tones also work with the gridding option, which already exists in the space. ๐Ÿš€ The application of tones depends on the quality and detail of the prompt given. The palette may be distorted in some cases. ๐Ÿš€It doesn't apply like a hue or tint in RGBA (as shown in canva app below); it is purely based on the prompts passed. ๐Ÿž๏ธ Check out the space: https://huggingface.co/spaces/prithivMLmods/IMAGINEO-4K ๐Ÿœ๏ธCollection: https://huggingface.co/collections/prithivMLmods/collection-zero-65e48a7dd8212873836ceca2 ``` huggingface.co/spaces/prithivMLmods/IMAGINEO-4K ``` ๐Ÿž๏ธWhat you can do with this space: โœ… Compose Image Grid ๐Ÿ‘‰๐Ÿป "2x1", "1x2", "2x2", "2x3", "3x2", "1x1" โœ… Apply styles โœ… Set up Image tones โœ… Apply filters & adjust quality . . . Thanks for reading! - @prithivMLmods
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2024-07-15T23:47:45.000Z
2024-08-06T09:59:54.161Z
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/posts/prithivMLmods/737813976382134
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[ { "type": "text", "value": "Introducing Plugins in NiansuhAI (on July 20, 2024)", "raw": "Introducing Plugins in NiansuhAI (on July 20, 2024)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Plugin Names:", "raw": "Plugin Names:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "1. WebSearch: Tool for searching the web using search engines.", "raw": "1. WebSearch: Tool for searching the web using search engines.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "2. Calculator: Helps evaluate mathematical expressions; extends the base Tool class.", "raw": "2. Calculator: Helps evaluate mathematical expressions; extends the base Tool class.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "3. WebBrowser: Interacts with web pages to extract information or summarize content.", "raw": "3. WebBrowser: Interacts with web pages to extract information or summarize content.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "4. Wikipedia: Retrieves data from Wikipedia using its API.", "raw": "4. Wikipedia: Retrieves data from Wikipedia using its API.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "5. Arxiv: Searches and fetches article information from Arxiv.", "raw": "5. Arxiv: Searches and fetches article information from Arxiv.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "6. WolframAlphaTool: Answers questions on Math, Science, Technology, Culture, Society, and Everyday Life.", "raw": "6. WolframAlphaTool: Answers questions on Math, Science, Technology, Culture, Society, and Everyday Life.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Similar to ", "raw": "Similar to ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://hf.co/chat", "resource": null, "url": null, "href": "https://hf.co/chat", "user": null, "lang": null, "code": null, "label": null } ]
Introducing Plugins in NiansuhAI (on July 20, 2024) Plugin Names: 1. WebSearch: Tool for searching the web using search engines. 2. Calculator: Helps evaluate mathematical expressions; extends the base Tool class. 3. WebBrowser: Interacts with web pages to extract information or summarize content. 4. Wikipedia: Retrieves data from Wikipedia using its API. 5. Arxiv: Searches and fetches article information from Arxiv. 6. WolframAlphaTool: Answers questions on Math, Science, Technology, Culture, Society, and Everyday Life. Similar to https://hf.co/chat
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2024-07-15T23:05:49.000Z
2024-09-02T12:16:41.286Z
[]
/posts/Niansuh/626990036588926
2,595
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[ { "type": "text", "value": "๐Ÿš€๐Ÿ•บ๐ŸŒŸ New Research Alert - CVPR 2024 (Avatars Collection)! ๐ŸŒŸ๐Ÿ’ƒ๐Ÿš€", "raw": "๐Ÿš€๐Ÿ•บ๐ŸŒŸ New Research Alert - CVPR 2024 (Avatars Collection)! ๐ŸŒŸ๐Ÿ’ƒ๐Ÿš€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“„ Title: IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing ๐Ÿ”", "raw": "๐Ÿ“„ Title: IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing ๐Ÿ”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ Description: IntrinsicAvatar is a method for extracting high-quality geometry, albedo, material, and lighting properties of clothed human avatars from monocular videos using explicit ray tracing and volumetric scattering, enabling realistic animations under varying lighting conditions.", "raw": "๐Ÿ“ Description: IntrinsicAvatar is a method for extracting high-quality geometry, albedo, material, and lighting properties of clothed human avatars from monocular videos using explicit ray tracing and volumetric scattering, enabling realistic animations under varying lighting conditions.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘ฅ Authors: Shaofei Wang, Boลพidar Antiฤ‡, Andreas Geiger, and Siyu Tang", "raw": "๐Ÿ‘ฅ Authors: Shaofei Wang, Boลพidar Antiฤ‡, Andreas Geiger, and Siyu Tang", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“… Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA ๐Ÿ‡บ๐Ÿ‡ธ", "raw": "๐Ÿ“… Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA ๐Ÿ‡บ๐Ÿ‡ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”— Paper: ", "raw": "๐Ÿ”— Paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2312.05210", "resource": { "type": "paper", "id": "2312.05210", "discussionNum": null }, "url": "https://huggingface.co/papers/2312.05210", "href": null, "user": null, "lang": null, "code": null, "label": "IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans\n from Monocular Videos via Explicit Ray Tracing (2312.05210)" }, { "type": 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"user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/taconite/IntrinsicAvatar", "resource": null, "url": null, "href": "https://github.com/taconite/IntrinsicAvatar", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“บ Video: ", "raw": "๐Ÿ“บ Video: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.youtube.com/watch?v=aS8AIxgVXzI", "resource": null, "url": null, "href": "https://www.youtube.com/watch?v=aS8AIxgVXzI", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ CVPR-2023-24-Papers: ", "raw": "๐Ÿš€ CVPR-2023-24-Papers: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/DmitryRyumin/CVPR-2023-24-Papers", "resource": null, "url": null, "href": "https://github.com/DmitryRyumin/CVPR-2023-24-Papers", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ WACV-2024-Papers: ", "raw": "๐Ÿš€ WACV-2024-Papers: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/DmitryRyumin/WACV-2024-Papers", "resource": null, "url": null, "href": "https://github.com/DmitryRyumin/WACV-2024-Papers", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ ICCV-2023-Papers: ", "raw": "๐Ÿš€ ICCV-2023-Papers: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/DmitryRyumin/ICCV-2023-Papers", "resource": null, "url": null, "href": "https://github.com/DmitryRyumin/ICCV-2023-Papers", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "raw": "๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "resource": { "type": "space", "id": "DmitryRyumin/NewEraAI-Papers", "discussionNum": null }, "url": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " curated by ", "raw": " curated by ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@DmitryRyumin", "resource": null, "url": null, "href": null, "user": "DmitryRyumin", "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ Added to the Avatars Collection: ", "raw": "๐Ÿš€ Added to the Avatars Collection: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "resource": { "type": "collection", "id": "DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "discussionNum": null }, "url": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ” Keywords: #IntrinsicAvatar #InverseRendering #MonocularVideos #RayTracing #VolumetricScattering #3DReconstruction #MachineLearning #ComputerVision #DeepLearning #AI #CVPR2024", "raw": "๐Ÿ” Keywords: #IntrinsicAvatar #InverseRendering #MonocularVideos #RayTracing #VolumetricScattering #3DReconstruction #MachineLearning #ComputerVision #DeepLearning #AI #CVPR2024", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿš€๐Ÿ•บ๐ŸŒŸ New Research Alert - CVPR 2024 (Avatars Collection)! ๐ŸŒŸ๐Ÿ’ƒ๐Ÿš€ ๐Ÿ“„ Title: IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing ๐Ÿ” ๐Ÿ“ Description: IntrinsicAvatar is a method for extracting high-quality geometry, albedo, material, and lighting properties of clothed human avatars from monocular videos using explicit ray tracing and volumetric scattering, enabling realistic animations under varying lighting conditions. ๐Ÿ‘ฅ Authors: Shaofei Wang, Boลพidar Antiฤ‡, Andreas Geiger, and Siyu Tang ๐Ÿ“… Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA ๐Ÿ‡บ๐Ÿ‡ธ ๐Ÿ”— Paper: https://huggingface.co/papers/2312.05210 ๐ŸŒ Github Page: https://neuralbodies.github.io/IntrinsicAvatar/ ๐Ÿ“ Repository: https://github.com/taconite/IntrinsicAvatar ๐Ÿ“บ Video: https://www.youtube.com/watch?v=aS8AIxgVXzI ๐Ÿš€ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers ๐Ÿš€ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers ๐Ÿš€ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers ๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin ๐Ÿš€ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36 ๐Ÿ” Keywords: #IntrinsicAvatar #InverseRendering #MonocularVideos #RayTracing #VolumetricScattering #3DReconstruction #MachineLearning #ComputerVision #DeepLearning #AI #CVPR2024
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2024-07-15T18:58:21.000Z
2024-07-15T18:58:21.225Z
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/posts/DmitryRyumin/211717085209386
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I am now a huggingface fellow ๐Ÿฅณ
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2024-07-15T18:07:05.000Z
2024-09-04T12:52:11.797Z
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[ { "type": "text", "value": "Exciting news for audio AI enthusiasts! ๐ŸŽ™๏ธ๐ŸŒ", "raw": "Exciting news for audio AI enthusiasts! ๐ŸŽ™๏ธ๐ŸŒ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The Emilia dataset dropped last week, and it's a cool one:", "raw": "The Emilia dataset dropped last week, and it's a cool one:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 101k+ hours of high-quality audio", "raw": "- 101k+ hours of high-quality audio", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- 6 languages: ๐Ÿ‡จ๐Ÿ‡ณ ๐Ÿ‡บ๐Ÿ‡ธ ๐Ÿ‡ฏ๐Ÿ‡ต ๐Ÿ‡ฐ๐Ÿ‡ท ๐Ÿ‡ฉ๐Ÿ‡ช ๐Ÿ‡ซ๐Ÿ‡ท", "raw": "- 6 languages: ๐Ÿ‡จ๐Ÿ‡ณ ๐Ÿ‡บ๐Ÿ‡ธ ๐Ÿ‡ฏ๐Ÿ‡ต ๐Ÿ‡ฐ๐Ÿ‡ท ๐Ÿ‡ฉ๐Ÿ‡ช ๐Ÿ‡ซ๐Ÿ‡ท", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Diverse content: talk shows, interviews, debates, sports commentary, audiobooks", "raw": "- Diverse content: talk shows, interviews, debates, sports commentary, audiobooks", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This dataset could improve multilingual speech generation and recognition. Opens up many possibilities for global media, language learning, and accessibility!", "raw": "This dataset could improve multilingual speech generation and recognition. Opens up many possibilities for global media, language learning, and accessibility!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Explore it: ", "raw": "Explore it: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/amphion/Emilia", "resource": { "type": "dataset", "id": "amphion/Emilia", "discussionNum": null }, "url": "https://huggingface.co/datasets/amphion/Emilia", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "#AIAudio", "raw": "#AIAudio", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Exciting news for audio AI enthusiasts! ๐ŸŽ™๏ธ๐ŸŒ The Emilia dataset dropped last week, and it's a cool one: - 101k+ hours of high-quality audio - 6 languages: ๐Ÿ‡จ๐Ÿ‡ณ ๐Ÿ‡บ๐Ÿ‡ธ ๐Ÿ‡ฏ๐Ÿ‡ต ๐Ÿ‡ฐ๐Ÿ‡ท ๐Ÿ‡ฉ๐Ÿ‡ช ๐Ÿ‡ซ๐Ÿ‡ท - Diverse content: talk shows, interviews, debates, sports commentary, audiobooks This dataset could improve multilingual speech generation and recognition. Opens up many possibilities for global media, language learning, and accessibility! Explore it: https://huggingface.co/datasets/amphion/Emilia #AIAudio
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2024-07-15T15:50:56.000Z
2024-07-15T15:50:56.673Z
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/posts/fdaudens/778836619881171
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[ { "type": "text", "value": "๐Ÿ”ฅ ๐Ÿ”ฅ๐Ÿ”ฅ", "raw": "๐Ÿ”ฅ ๐Ÿ”ฅ๐Ÿ”ฅ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Excited to announce WizardLM new Paper: Auto Evol-Instruct!", "raw": "Excited to announce WizardLM new Paper: Auto Evol-Instruct!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿฆ Twitter: ", "raw": "๐Ÿฆ Twitter: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://x.com/WizardLM_AI/status/1812857977122202087", "resource": null, "url": null, "href": "https://x.com/WizardLM_AI/status/1812857977122202087", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ƒ Paper: ", "raw": "๐Ÿ“ƒ Paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/pdf/2406.00770", "resource": null, "url": null, "href": "https://arxiv.org/pdf/2406.00770", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿค– 1. Fully AI-Powered Pipeline", "raw": "๐Ÿค– 1. Fully AI-Powered Pipeline", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Auto Evol-Instruct automatically involves an iterative process of optimizing an Evol-Instruct V1 into an optimal one. The pipeline consists of two critical stages: Evol Trajectory Analysis, where the optimizer LLM analyzes the issues and failures exposed in instruction evolution performed by the evol LLM, and Evolving Method Optimization, where the optimizer LLM addresses these issues to progressively develop an effective evolving method. The optimal evolving method is then used to convert the entire instruction dataset into more diverse and complex forms, facilitating improved instruction tuning.", "raw": "Auto Evol-Instruct automatically involves an iterative process of optimizing an Evol-Instruct V1 into an optimal one. The pipeline consists of two critical stages: Evol Trajectory Analysis, where the optimizer LLM analyzes the issues and failures exposed in instruction evolution performed by the evol LLM, and Evolving Method Optimization, where the optimizer LLM addresses these issues to progressively develop an effective evolving method. The optimal evolving method is then used to convert the entire instruction dataset into more diverse and complex forms, facilitating improved instruction tuning.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“ˆ2. Scaling Evol-Instruct with Arena Learning", "raw": "๐Ÿ“ˆ2. Scaling Evol-Instruct with Arena Learning", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "With Auto Evol-Instruct, the evolutionary synthesis data of WizardLM-2 has scaled up from WizardLM-1 to dozens of domains, covering tasks in all aspects of large language models. This allows Arena Learning to train and learn from an almost infinite pool of high-difficulty instruction data, fully unlocking all the potential of Arena Learning.", "raw": "With Auto Evol-Instruct, the evolutionary synthesis data of WizardLM-2 has scaled up from WizardLM-1 to dozens of domains, covering tasks in all aspects of large language models. This allows Arena Learning to train and learn from an almost infinite pool of high-difficulty instruction data, fully unlocking all the potential of Arena Learning.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿ”ฅ ๐Ÿ”ฅ๐Ÿ”ฅ Excited to announce WizardLM new Paper: Auto Evol-Instruct! ๐Ÿฆ Twitter: https://x.com/WizardLM_AI/status/1812857977122202087 ๐Ÿ“ƒ Paper: https://arxiv.org/pdf/2406.00770 ๐Ÿค– 1. Fully AI-Powered Pipeline Auto Evol-Instruct automatically involves an iterative process of optimizing an Evol-Instruct V1 into an optimal one. The pipeline consists of two critical stages: Evol Trajectory Analysis, where the optimizer LLM analyzes the issues and failures exposed in instruction evolution performed by the evol LLM, and Evolving Method Optimization, where the optimizer LLM addresses these issues to progressively develop an effective evolving method. The optimal evolving method is then used to convert the entire instruction dataset into more diverse and complex forms, facilitating improved instruction tuning. ๐Ÿ“ˆ2. Scaling Evol-Instruct with Arena Learning With Auto Evol-Instruct, the evolutionary synthesis data of WizardLM-2 has scaled up from WizardLM-1 to dozens of domains, covering tasks in all aspects of large language models. This allows Arena Learning to train and learn from an almost infinite pool of high-difficulty instruction data, fully unlocking all the potential of Arena Learning.
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2024-07-15T15:42:45.000Z
2024-10-28T10:09:16.328Z
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/posts/WizardLM/574698793995338
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[ { "type": "text", "value": "๐Ÿš€ Meet Our New Line of Efficient and Accurate Zero-Shot Classifiers! ๐Ÿš€", "raw": "๐Ÿš€ Meet Our New Line of Efficient and Accurate Zero-Shot Classifiers! ๐Ÿš€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The new architecture brings better inter-label understanding and can solve complex classification tasks at a single forward pass.", "raw": "The new architecture brings better inter-label understanding and can solve complex classification tasks at a single forward pass.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Key Applications:", "raw": "Key Applications:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Multi-class classification (up to 100 classes in a single run)", "raw": "โœ… Multi-class classification (up to 100 classes in a single run)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Topic classification", "raw": "โœ… Topic classification", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Sentiment analysis", "raw": "โœ… Sentiment analysis", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Event classification", "raw": "โœ… Event classification", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Prompt-based constrained classification", "raw": "โœ… Prompt-based constrained classification", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Natural Language Inference", "raw": "โœ… Natural Language Inference", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "โœ… Multi- and single-label classification", "raw": "โœ… Multi- and single-label classification", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/knowledgator/gliclass-6661838823756265f2ac3848", "resource": { "type": "collection", "id": "knowledgator/gliclass-6661838823756265f2ac3848", "discussionNum": null }, "url": "https://huggingface.co/collections/knowledgator/gliclass-6661838823756265f2ac3848", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/knowledgator/GLiClass_SandBox", "resource": { "type": "space", "id": "knowledgator/GLiClass_SandBox", "discussionNum": null }, "url": "https://huggingface.co/spaces/knowledgator/GLiClass_SandBox", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/knowledgator/gliclass-base-v1.0-lw", "resource": { "type": "model", "id": "knowledgator/gliclass-base-v1.0-lw", "discussionNum": null }, "url": "https://huggingface.co/knowledgator/gliclass-base-v1.0-lw", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿš€ Meet Our New Line of Efficient and Accurate Zero-Shot Classifiers! ๐Ÿš€ The new architecture brings better inter-label understanding and can solve complex classification tasks at a single forward pass. Key Applications: โœ… Multi-class classification (up to 100 classes in a single run) โœ… Topic classification โœ… Sentiment analysis โœ… Event classification โœ… Prompt-based constrained classification โœ… Natural Language Inference โœ… Multi- and single-label classification https://huggingface.co/collections/knowledgator/gliclass-6661838823756265f2ac3848 https://huggingface.co/spaces/knowledgator/GLiClass_SandBox https://huggingface.co/knowledgator/gliclass-base-v1.0-lw
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2024-07-15T09:29:57.000Z
2024-07-15T09:30:42.028Z
[]
/posts/Ihor/615276720615026
887
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408808506555116
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@seyonec It's a great help to experiment with ChemBERTa. BTW, There are several models that handle SMILES in the model repository. Can you kindly recommend the one with the best performance in handling hERG dataset? https://paperswithcode.com/dataset/herg Best, Joo-Haeng Lee, Pebblous Inc. http://pebblous.ai
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2024-07-15T06:40:50.000Z
2024-07-15T06:40:50.723Z
[]
/posts/joohaeng/408808506555116
831
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358759970482010
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@theroot srry i accidentally tried to join your group-of-one, pls ignore :) but ur work seems super interesting!
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2024-07-14T21:10:11.000Z
2024-07-15T03:24:38.215Z
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/posts/larryl/358759970482010
712
1
414464833451294
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Updated https://huggingface.co/blog/nroggendorff/train-with-llama-architecture so you can "train" your own tokenizer from your dataset.
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2024-07-14T18:09:03.000Z
2024-07-16T03:29:01.604Z
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/posts/nroggendorff/414464833451294
2,052
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[ { "type": "text", "value": "New model drop...๐Ÿฅ", "raw": "New model drop...๐Ÿฅ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "FROSTING LANE REDUX", "raw": "FROSTING LANE REDUX", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The v1 of this model was released during a big model push, so I think it got lost in the shuffle. I revisited it for a project and realized it wasn't inventive enough around certain concepts, so I decided to retrain.", "raw": "The v1 of this model was released during a big model push, so I think it got lost in the shuffle. I revisited it for a project and realized it wasn't inventive enough around certain concepts, so I decided to retrain.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/alvdansen/frosting_lane_redux", "resource": { "type": "model", "id": "alvdansen/frosting_lane_redux", "discussionNum": null }, "url": "https://huggingface.co/alvdansen/frosting_lane_redux", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I think the original model was really strong on it's own, but because it was trained on fewer images I found that it was producing a very lackluster range of facial expressions, so I wanted to improve that.", "raw": "I think the original model was really strong on it's own, but because it was trained on fewer images I found that it was producing a very lackluster range of facial expressions, so I wanted to improve that.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The hardest part of creating models like this, I find, is maintaining the detailed linework without without overfitting. It takes a really balanced dataset and I repeat the data 12 times during the process, stopping at the last 10-20 epochs.", "raw": "The hardest part of creating models like this, I find, is maintaining the detailed linework without without overfitting. It takes a really balanced dataset and I repeat the data 12 times during the process, stopping at the last 10-20 epochs.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "It is very difficult to predict the exact amount of time needed, so for me it is crucial to do epoch stops. Every model has a different threshold for ideal success.", "raw": "It is very difficult to predict the exact amount of time needed, so for me it is crucial to do epoch stops. Every model has a different threshold for ideal success.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
New model drop...๐Ÿฅ FROSTING LANE REDUX The v1 of this model was released during a big model push, so I think it got lost in the shuffle. I revisited it for a project and realized it wasn't inventive enough around certain concepts, so I decided to retrain. https://huggingface.co/alvdansen/frosting_lane_redux I think the original model was really strong on it's own, but because it was trained on fewer images I found that it was producing a very lackluster range of facial expressions, so I wanted to improve that. The hardest part of creating models like this, I find, is maintaining the detailed linework without without overfitting. It takes a really balanced dataset and I repeat the data 12 times during the process, stopping at the last 10-20 epochs. It is very difficult to predict the exact amount of time needed, so for me it is crucial to do epoch stops. Every model has a different threshold for ideal success.
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2024-07-14T14:23:01.000Z
2024-07-14T14:23:01.456Z
[]
/posts/alvdansen/871982135472231
4,525
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"https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ” Keywords: #RodinHD #3DAvatars #DiffusionModels #HighFidelity #PortraitTo3D #MachineLearning #ComputerVision #DeepLearning #AI #ECCV2024", "raw": "๐Ÿ” Keywords: #RodinHD #3DAvatars #DiffusionModels #HighFidelity #PortraitTo3D #MachineLearning #ComputerVision #DeepLearning #AI #ECCV2024", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿ”ฅ๐ŸŽญ๐ŸŒŸ New Research Alert - ECCV 2024 (Avatars Collection)! ๐ŸŒŸ๐ŸŽญ๐Ÿ”ฅ ๐Ÿ“„ Title: RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models ๐Ÿ” ๐Ÿ“ Description: RodinHD generates high-fidelity 3D avatars from portrait images using a novel data scheduling strategy and weight consolidation regularization to capture intricate details such as hairstyles. ๐Ÿ‘ฅ Authors: Bowen Zhang, @yiji, @chunyuwang, Ting Zhang, @jiaolong, Yansong Tang, Feng Zhao, Dong Chen, and Baining Guo ๐Ÿ“… Conference: ECCV, 29 Sep โ€“ 4 Oct, 2024 | Milano, Italy ๐Ÿ‡ฎ๐Ÿ‡น ๐Ÿ“„ Paper: https://huggingface.co/papers/2407.06938 ๐ŸŒ Github Page: https://rodinhd.github.io/ ๐Ÿ“ Repository: https://github.com/RodinHD/RodinHD ๐Ÿ“บ Video: https://www.youtube.com/watch?v=ULvHt7dZx-Q ๐Ÿš€ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers ๐Ÿš€ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers ๐Ÿš€ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers ๐Ÿ“š More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin ๐Ÿš€ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36 ๐Ÿ” Keywords: #RodinHD #3DAvatars #DiffusionModels #HighFidelity #PortraitTo3D #MachineLearning #ComputerVision #DeepLearning #AI #ECCV2024
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2024-07-15T18:57:28.843Z
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[ { "type": "text", "value": "Responsibly building AI also means knowing its impact on the environment and the hidden carbon costs associated with it๐ŸŒฑ ", "raw": "Responsibly building AI also means knowing its impact on the environment and the hidden carbon costs associated with it๐ŸŒฑ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "If you're interested in the subject, you can check out my latest community article: ", "raw": "If you're interested in the subject, you can check out my latest community article: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/as-cle-bert/is-ai-carbon-footprint-worrisome", "resource": null, "url": null, "href": "https://huggingface.co/blog/as-cle-bert/is-ai-carbon-footprint-worrisome", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Where I try to unravel AI's carbon footprint and potential solutions to reduce it๐ŸŒป", "raw": "Where I try to unravel AI's carbon footprint and potential solutions to reduce it๐ŸŒป", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Enjoy!๐Ÿค—", "raw": "Enjoy!๐Ÿค—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Responsibly building AI also means knowing its impact on the environment and the hidden carbon costs associated with it๐ŸŒฑ If you're interested in the subject, you can check out my latest community article: https://huggingface.co/blog/as-cle-bert/is-ai-carbon-footprint-worrisome Where I try to unravel AI's carbon footprint and potential solutions to reduce it๐ŸŒป Enjoy!๐Ÿค—
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2024-07-14T12:29:11.531Z
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[ { "type": "text", "value": "Introducing: 'Synthetic Math Phi'! Literally just press a button, and receive 10 random math problems and 10 random answers, outputted in JSON format and ready to be fed back to an LLM model. ", "raw": "Introducing: 'Synthetic Math Phi'! Literally just press a button, and receive 10 random math problems and 10 random answers, outputted in JSON format and ready to be fed back to an LLM model. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This app is inspired by the research in this research paper. Their findings show that in order to increase mathematical reasoning of models, we need to give them more math. Quality is kind of not the important factor (for math only). Not enough math exists currently to train the models, that is the bottleneck. So, press a button and generate some math for free! ", "raw": "This app is inspired by the research in this research paper. Their findings show that in order to increase mathematical reasoning of models, we need to give them more math. Quality is kind of not the important factor (for math only). Not enough math exists currently to train the models, that is the bottleneck. So, press a button and generate some math for free! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "MIT licensed, here is the Github: ", "raw": "MIT licensed, here is the Github: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/RichardAragon/Synthetic-Math-Phi", "resource": null, "url": null, "href": "https://github.com/RichardAragon/Synthetic-Math-Phi", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ", "raw": " ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Introducing: 'Synthetic Math Phi'! Literally just press a button, and receive 10 random math problems and 10 random answers, outputted in JSON format and ready to be fed back to an LLM model. This app is inspired by the research in this research paper. Their findings show that in order to increase mathematical reasoning of models, we need to give them more math. Quality is kind of not the important factor (for math only). Not enough math exists currently to train the models, that is the bottleneck. So, press a button and generate some math for free! MIT licensed, here is the Github: https://github.com/RichardAragon/Synthetic-Math-Phi
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2024-07-14T09:52:41.000Z
2024-07-18T20:59:08.944Z
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/posts/TuringsSolutions/737250440678858
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Check out this SD3 Inpainting space I've created: https://huggingface.co/spaces/Kkordik/inpainting-sd3-sketch-pad
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2024-07-13T10:51:34.000Z
2024-07-13T10:51:34.556Z
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[ { "type": "text", "value": "Large models are surprisingly bad storytellers.", "raw": "Large models are surprisingly bad storytellers.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I asked 8 LLMs to \"Tell me a bedtime story about bears and waffles.\"", "raw": "I asked 8 LLMs to \"Tell me a bedtime story about bears and waffles.\"", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Claude 3.5 Sonnet and GPT-4o gave me the worst stories: no conflict, no moral, zero creativity.", "raw": "Claude 3.5 Sonnet and GPT-4o gave me the worst stories: no conflict, no moral, zero creativity.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "In contrast, smaller models were quite creative and wrote stories involving talking waffle trees and bears ostracized for their love of waffles.", "raw": "In contrast, smaller models were quite creative and wrote stories involving talking waffle trees and bears ostracized for their love of waffles.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Here you can see a comparison between Claude 3.5 Sonnet and NeuralDaredevil-8B-abliterated. They both start with a family of bears but quickly diverge in terms of personality, conflict, etc.", "raw": "Here you can see a comparison between Claude 3.5 Sonnet and NeuralDaredevil-8B-abliterated. They both start with a family of bears but quickly diverge in terms of personality, conflict, etc.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I mapped it to the hero's journey to have some kind of framework. Prompt engineering can definitely help here, but it's still disappointing that the larger models don't create better stories right off the bat.", "raw": "I mapped it to the hero's journey to have some kind of framework. Prompt engineering can definitely help here, but it's still disappointing that the larger models don't create better stories right off the bat.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Do you know why smaller models outperform the frontier models here?", "raw": "Do you know why smaller models outperform the frontier models here?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Large models are surprisingly bad storytellers. I asked 8 LLMs to "Tell me a bedtime story about bears and waffles." Claude 3.5 Sonnet and GPT-4o gave me the worst stories: no conflict, no moral, zero creativity. In contrast, smaller models were quite creative and wrote stories involving talking waffle trees and bears ostracized for their love of waffles. Here you can see a comparison between Claude 3.5 Sonnet and NeuralDaredevil-8B-abliterated. They both start with a family of bears but quickly diverge in terms of personality, conflict, etc. I mapped it to the hero's journey to have some kind of framework. Prompt engineering can definitely help here, but it's still disappointing that the larger models don't create better stories right off the bat. Do you know why smaller models outperform the frontier models here?
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2024-07-13T10:33:24.000Z
2024-09-25T17:08:55.873Z
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[ { "type": "text", "value": "๐ŸŽ‰ The Ghost 8B Beta model outperforms prominent models such as Llama 3 8B Instruct, GPT 3.5 Turbo in the lc_winrate score. In addition, it also outperforms Claude 3 Opus, Claude 3 Sonnet, GPT-4, and Mistral Large when comparing the winrate score of AlpacaEval 2.0.", "raw": "๐ŸŽ‰ The Ghost 8B Beta model outperforms prominent models such as Llama 3 8B Instruct, GPT 3.5 Turbo in the lc_winrate score. In addition, it also outperforms Claude 3 Opus, Claude 3 Sonnet, GPT-4, and Mistral Large when comparing the winrate score of AlpacaEval 2.0.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Ghost 8B Beta is a large language model developed with goals that include excellent multilingual support, superior knowledge capabilities, and cost-effectiveness. The model comes in two context length versions, 8k and 128k, along with multilingual function tools support by default.", "raw": "Ghost 8B Beta is a large language model developed with goals that include excellent multilingual support, superior knowledge capabilities, and cost-effectiveness. The model comes in two context length versions, 8k and 128k, along with multilingual function tools support by default.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The languages supported are ๐Ÿ‡บ๐Ÿ‡ธ English, ๐Ÿ‡ซ๐Ÿ‡ท French, ๐Ÿ‡ฎ๐Ÿ‡น Italian, ๐Ÿ‡ช๐Ÿ‡ธ Spanish, ๐Ÿ‡ต๐Ÿ‡น Portuguese, ๐Ÿ‡ฉ๐Ÿ‡ช German, ๐Ÿ‡ป๐Ÿ‡ณ Vietnamese, ๐Ÿ‡ฐ๐Ÿ‡ท Korean and ๐Ÿ‡จ๐Ÿ‡ณ Chinese.", "raw": "The languages supported are ๐Ÿ‡บ๐Ÿ‡ธ English, ๐Ÿ‡ซ๐Ÿ‡ท French, ๐Ÿ‡ฎ๐Ÿ‡น Italian, ๐Ÿ‡ช๐Ÿ‡ธ Spanish, ๐Ÿ‡ต๐Ÿ‡น Portuguese, ๐Ÿ‡ฉ๐Ÿ‡ช German, ๐Ÿ‡ป๐Ÿ‡ณ Vietnamese, ๐Ÿ‡ฐ๐Ÿ‡ท Korean and ๐Ÿ‡จ๐Ÿ‡ณ Chinese.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Explore the Potential:", "raw": "Explore the Potential:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "To learn more about this groundbreaking language model, visit the official website or explore the online demo platforms:", "raw": "To learn more about this groundbreaking language model, visit the official website or explore the online demo platforms:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Ghost 8B Beta (ฮฒ, 8k) on Spaces: ", "raw": "- Ghost 8B Beta (ฮฒ, 8k) on Spaces: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k", "resource": { "type": "space", "id": "lamhieu/ghost-8b-beta-8k", "discussionNum": null }, "url": "https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ".", "raw": ".", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Ghost 8B Beta (ฮฒ, 128k) on Spaces: ", "raw": "- Ghost 8B Beta (ฮฒ, 128k) on Spaces: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/lamhieu/ghost-8b-beta-128k", "resource": { "type": "space", "id": "lamhieu/ghost-8b-beta-128k", "discussionNum": null }, "url": "https://huggingface.co/spaces/lamhieu/ghost-8b-beta-128k", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Official website: ", "raw": "- Official website: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://ghost-x.org/docs/models/ghost-8b-beta", "resource": null, "url": null, "href": "https://ghost-x.org/docs/models/ghost-8b-beta", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐ŸŽ‰ The Ghost 8B Beta model outperforms prominent models such as Llama 3 8B Instruct, GPT 3.5 Turbo in the lc_winrate score. In addition, it also outperforms Claude 3 Opus, Claude 3 Sonnet, GPT-4, and Mistral Large when comparing the winrate score of AlpacaEval 2.0. Ghost 8B Beta is a large language model developed with goals that include excellent multilingual support, superior knowledge capabilities, and cost-effectiveness. The model comes in two context length versions, 8k and 128k, along with multilingual function tools support by default. The languages supported are ๐Ÿ‡บ๐Ÿ‡ธ English, ๐Ÿ‡ซ๐Ÿ‡ท French, ๐Ÿ‡ฎ๐Ÿ‡น Italian, ๐Ÿ‡ช๐Ÿ‡ธ Spanish, ๐Ÿ‡ต๐Ÿ‡น Portuguese, ๐Ÿ‡ฉ๐Ÿ‡ช German, ๐Ÿ‡ป๐Ÿ‡ณ Vietnamese, ๐Ÿ‡ฐ๐Ÿ‡ท Korean and ๐Ÿ‡จ๐Ÿ‡ณ Chinese. Explore the Potential: To learn more about this groundbreaking language model, visit the official website or explore the online demo platforms: - Ghost 8B Beta (ฮฒ, 8k) on Spaces: https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k. - Ghost 8B Beta (ฮฒ, 128k) on Spaces: https://huggingface.co/spaces/lamhieu/ghost-8b-beta-128k - Official website: https://ghost-x.org/docs/models/ghost-8b-beta
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2024-07-23T02:12:06.344Z
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/posts/lamhieu/665588664558445
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Use GPT-4, GPT-4 Turbo Preview, GPT-3.5 Turbo, BingAI, and other models. The interface is similar to ChatGPT, with a speedy API endpoint. https://huggingface.co/spaces/NiansuhAI/Copilot
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2024-07-12T07:05:40.000Z
2024-07-13T14:38:27.433Z
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/posts/Niansuh/998648786188551
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Announcing the second open model in our Aura series of media models at @fal: https://hf.co/fal/AuraFlow Try it using diffusers or ComfyUI from publicly available weights, and read more about it in our blog https://blog.fal.ai/auraflow.
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2024-07-12T01:42:50.000Z
2024-07-15T09:52:54.234Z
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/posts/isidentical/577595746625472
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[ { "type": "text", "value": "Hello!", "raw": "Hello!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "I've been in the lab synthesizing captions, with my trusty sidekick Blip, and along the way I had an interesting idea. I thought of designing an incredibly simple model that accepts simple instruction pairs, adjective noun pairs specifically, and outputs 2d vertices.", "raw": "I've been in the lab synthesizing captions, with my trusty sidekick Blip, and along the way I had an interesting idea. I thought of designing an incredibly simple model that accepts simple instruction pairs, adjective noun pairs specifically, and outputs 2d vertices.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The current implementation has been implemented by myself then ran over with Claude, not because I am incompetent, but because I recognize tools written by experts may have more technique than my newbie self.", "raw": "The current implementation has been implemented by myself then ran over with Claude, not because I am incompetent, but because I recognize tools written by experts may have more technique than my newbie self.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "As with all projects, this will be updated with proportion to the feedback received, if someone's using it and wants to keep using it, i'm happy to keep working on anything. Thanks, all! ๐Ÿค—", "raw": "As with all projects, this will be updated with proportion to the feedback received, if someone's using it and wants to keep using it, i'm happy to keep working on anything. Thanks, all! ๐Ÿค—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "inline_code", "value": null, "raw": "`-<3`", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": "-<3", "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/gist/SMeyersMrOvkill/8d4686db803f6c5f43fafc1c94b1c8c6/polypathdelement.ipynb", "resource": null, "url": null, "href": "https://colab.research.google.com/gist/SMeyersMrOvkill/8d4686db803f6c5f43fafc1c94b1c8c6/polypathdelement.ipynb", "user": null, "lang": null, "code": null, "label": null } ]
Hello! I've been in the lab synthesizing captions, with my trusty sidekick Blip, and along the way I had an interesting idea. I thought of designing an incredibly simple model that accepts simple instruction pairs, adjective noun pairs specifically, and outputs 2d vertices. The current implementation has been implemented by myself then ran over with Claude, not because I am incompetent, but because I recognize tools written by experts may have more technique than my newbie self. As with all projects, this will be updated with proportion to the feedback received, if someone's using it and wants to keep using it, i'm happy to keep working on anything. Thanks, all! ๐Ÿค— `-<3` https://colab.research.google.com/gist/SMeyersMrOvkill/8d4686db803f6c5f43fafc1c94b1c8c6/polypathdelement.ipynb
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2024-07-12T00:07:01.000Z
2024-07-12T00:07:01.426Z
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/posts/MrOvkill/488439011407346
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Started a list of LLMs used to make HackerNoon happen: https://huggingface.co/collections/HackerNoon/models-used-in-hackernoon-publishing-system-668c56a0d10c3be5d338b805
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[]
2024-07-11T20:52:15.000Z
2024-07-11T20:52:15.341Z
[]
/posts/Smooke/249450160845232
886
0
482171531718772
[ { "type": "text", "value": "๐Ÿš€ I'm excited to announce that huggingface_hub's InferenceClient now supports OpenAI's Python client syntax! For developers integrating AI into their codebases, this means you can switch to open-source models with just three lines of code. Here's a quick example of how easy it is.", "raw": "๐Ÿš€ I'm excited to announce that huggingface_hub's InferenceClient now supports OpenAI's Python client syntax! For developers integrating AI into their codebases, this means you can switch to open-source models with just three lines of code. Here's a quick example of how easy it is.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Why use the InferenceClient?", "raw": "Why use the InferenceClient?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”„ Seamless transition: keep your existing code structure while leveraging LLMs hosted on the Hugging Face Hub.", "raw": "๐Ÿ”„ Seamless transition: keep your existing code structure while leveraging LLMs hosted on the Hugging Face Hub.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿค— Direct integration: easily launch a model to run inference using our Inference Endpoint service.", "raw": "๐Ÿค— Direct integration: easily launch a model to run inference using our Inference Endpoint service.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ Stay Updated: always be in sync with the latest Text-Generation-Inference (TGI) updates.", "raw": "๐Ÿš€ Stay Updated: always be in sync with the latest Text-Generation-Inference (TGI) updates.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "More details in ", "raw": "More details in ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/docs/huggingface_hub/main/en/guides/inference#openai-compatibility", "resource": null, "url": null, "href": "https://huggingface.co/docs/huggingface_hub/main/en/guides/inference#openai-compatibility", "user": null, "lang": null, "code": null, "label": null } ]
๐Ÿš€ I'm excited to announce that huggingface_hub's InferenceClient now supports OpenAI's Python client syntax! For developers integrating AI into their codebases, this means you can switch to open-source models with just three lines of code. Here's a quick example of how easy it is. Why use the InferenceClient? ๐Ÿ”„ Seamless transition: keep your existing code structure while leveraging LLMs hosted on the Hugging Face Hub. ๐Ÿค— Direct integration: easily launch a model to run inference using our Inference Endpoint service. ๐Ÿš€ Stay Updated: always be in sync with the latest Text-Generation-Inference (TGI) updates. More details in https://huggingface.co/docs/huggingface_hub/main/en/guides/inference#openai-compatibility
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2024-07-11T13:19:30.000Z
2024-07-16T08:56:22.345Z
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/posts/Wauplin/482171531718772
3,343
3
483674589268011
[ { "type": "text", "value": "Have you looked at the Gemini Nano local LLM?", "raw": "Have you looked at the Gemini Nano local LLM?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Gradio-Lite, the in-browser ver. of Gradio, gives it a rich interface using only Python code, even for such an in-browser AI app!", "raw": "Gradio-Lite, the in-browser ver. of Gradio, gives it a rich interface using only Python code, even for such an in-browser AI app!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Try out a chat app that runs completely inside your browser ๐Ÿ‘‡", "raw": "Try out a chat app that runs completely inside your browser ๐Ÿ‘‡", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://www.gradio.app/playground?demo=Hello_World&code=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", "resource": null, "url": null, "href": "https://www.gradio.app/playground?demo=Hello_World&code=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", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Note: Gemini Nano is currently only available on Chrome Canary, and you need to opt-in.", "raw": "Note: Gemini Nano is currently only available on Chrome Canary, and you need to opt-in.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Follow the \"Installation\" section in ", "raw": "Follow the \"Installation\" section in ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/Xenova/run-gemini-nano-in-your-browser", "resource": null, "url": null, "href": "https://huggingface.co/blog/Xenova/run-gemini-nano-in-your-browser", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " .", "raw": " .", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Have you looked at the Gemini Nano local LLM? Gradio-Lite, the in-browser ver. of Gradio, gives it a rich interface using only Python code, even for such an in-browser AI app! Try out a chat app that runs completely inside your browser ๐Ÿ‘‡ https://www.gradio.app/playground?demo=Hello_World&code=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 Note: Gemini Nano is currently only available on Chrome Canary, and you need to opt-in. Follow the "Installation" section in https://huggingface.co/blog/Xenova/run-gemini-nano-in-your-browser .
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2024-07-11T07:49:00.000Z
2024-07-12T09:35:25.900Z
[]
/posts/whitphx/483674589268011
2,271
0
787451241194394
[ { "type": "text", "value": "Still following your human intuition to mix corpora from different sources for pre-training ๐Ÿง ? Everyone says that data mixture has a big impact on model performance, but how - and why๐Ÿ•ต๏ธ? Did you know that web corpora are actually highly impactful for downstream tasks ๐Ÿ†?", "raw": "Still following your human intuition to mix corpora from different sources for pre-training ๐Ÿง ? Everyone says that data mixture has a big impact on model performance, but how - and why๐Ÿ•ต๏ธ? Did you know that web corpora are actually highly impactful for downstream tasks ๐Ÿ†?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Check out our preprint \"RegMix: Data Mixture as Regression for Language Model Pre-training\" ๐Ÿ“„", "raw": "Check out our preprint \"RegMix: Data Mixture as Regression for Language Model Pre-training\" ๐Ÿ“„", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ”ฌ In this paper, we've proposed an automatic data mixture method RegMix that achieves a 6.3% improvement over human selection on the widely used HellaSwag benchmark - and it only needs a 2% extra training FLOPs! ๐Ÿ“ˆ", "raw": "๐Ÿ”ฌ In this paper, we've proposed an automatic data mixture method RegMix that achieves a 6.3% improvement over human selection on the widely used HellaSwag benchmark - and it only needs a 2% extra training FLOPs! ๐Ÿ“ˆ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“„ Paper: ", "raw": "๐Ÿ“„ Paper: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2407.01492", "resource": { "type": "paper", "id": "2407.01492", "discussionNum": null }, "url": "https://huggingface.co/papers/2407.01492", "href": null, "user": null, "lang": null, "code": null, "label": "RegMix: Data Mixture as Regression for Language Model Pre-training (2407.01492)" }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’ป Code: ", "raw": "๐Ÿ’ป Code: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/sail-sg/regmix", "resource": null, "url": null, "href": "https://github.com/sail-sg/regmix", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ“Š Collection: ", "raw": "๐Ÿ“Š Collection: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/sail/regmix-data-mixture-as-regression-6682b6caab37b9442877f0ce", "resource": { "type": "collection", "id": "sail/regmix-data-mixture-as-regression-6682b6caab37b9442877f0ce", "discussionNum": null }, "url": "https://huggingface.co/collections/sail/regmix-data-mixture-as-regression-6682b6caab37b9442877f0ce", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ๐ŸŽฎ Demo: ", "raw": " ๐ŸŽฎ Demo: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/sail/RegMix", "resource": null, "url": null, "href": "https://huggingface.co/spaces/sail/RegMix", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Still following your human intuition to mix corpora from different sources for pre-training ๐Ÿง ? Everyone says that data mixture has a big impact on model performance, but how - and why๐Ÿ•ต๏ธ? Did you know that web corpora are actually highly impactful for downstream tasks ๐Ÿ†? Check out our preprint "RegMix: Data Mixture as Regression for Language Model Pre-training" ๐Ÿ“„ ๐Ÿ”ฌ In this paper, we've proposed an automatic data mixture method RegMix that achieves a 6.3% improvement over human selection on the widely used HellaSwag benchmark - and it only needs a 2% extra training FLOPs! ๐Ÿ“ˆ ๐Ÿ“„ Paper: https://huggingface.co/papers/2407.01492 ๐Ÿ’ป Code: https://github.com/sail-sg/regmix ๐Ÿ“Š Collection: https://huggingface.co/collections/sail/regmix-data-mixture-as-regression-6682b6caab37b9442877f0ce ๐ŸŽฎ Demo: https://huggingface.co/spaces/sail/RegMix
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2024-07-11T03:23:42.000Z
2024-07-11T03:26:37.619Z
[]
/posts/SivilTaram/787451241194394
2,439
0
127954299366444
[ { "type": "text", "value": "Hi everyone ๐Ÿค—", "raw": "Hi everyone ๐Ÿค—", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "You can easily use the new Model Explorer from Google (", "raw": "You can easily use the new Model Explorer from Google (", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/google-ai-edge/model-explorer", "resource": null, "url": null, "href": "https://github.com/google-ai-edge/model-explorer", "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": ") here: ", "raw": ") here: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/1aurent/model-explorer", "resource": { "type": "space", "id": "1aurent/model-explorer", "discussionNum": null }, "url": "https://huggingface.co/spaces/1aurent/model-explorer", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Unfortunately, it doesn't look like it can easily be preloaded with some models, so you'll have to bring your own. Here is a quick selection of models you can use:", "raw": "Unfortunately, it doesn't look like it can easily be preloaded with some models, so you'll have to bring your own. Here is a quick selection of models you can use:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/openai-community/gpt2/blob/main/64-8bits.tflite", "resource": { "type": "model", "id": "openai-community/gpt2", "discussionNum": null }, "url": "https://huggingface.co/openai-community/gpt2/blob/main/64-8bits.tflite", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite", "resource": { "type": "model", "id": "qualcomm/ResNet50", "discussionNum": null }, "url": "https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- ", "raw": "- ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite", "resource": { "type": "model", "id": "qualcomm/VIT", "discussionNum": null }, "url": "https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Hi everyone ๐Ÿค— You can easily use the new Model Explorer from Google (https://github.com/google-ai-edge/model-explorer) here: https://huggingface.co/spaces/1aurent/model-explorer Unfortunately, it doesn't look like it can easily be preloaded with some models, so you'll have to bring your own. Here is a quick selection of models you can use: - https://huggingface.co/openai-community/gpt2/blob/main/64-8bits.tflite - https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite - https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite
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[]
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2024-07-10T21:04:31.000Z
2024-07-10T21:30:29.490Z
[]
/posts/1aurent/127954299366444
2,003
0
514389095252830
[ { "type": "text", "value": "Korvus: Single-query RAG with Postgres", "raw": "Korvus: Single-query RAG with Postgres", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Hey folks! ", "raw": "Hey folks! ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We built Korvus, an open-source RAG (Retrieval-Augmented Generation) pipeline that consolidates the entire RAG workflow - from embedding generation to text generation - into a single SQL query, significantly reducing architectural complexity and latency.", "raw": "We built Korvus, an open-source RAG (Retrieval-Augmented Generation) pipeline that consolidates the entire RAG workflow - from embedding generation to text generation - into a single SQL query, significantly reducing architectural complexity and latency.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/postgresml/korvus", "resource": null, "url": null, "href": "https://github.com/postgresml/korvus", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Here's some of the highlights:", "raw": "Here's some of the highlights:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Full RAG pipeline (embedding generation, vector search, reranking, and text generation) in one SQL query", "raw": "- Full RAG pipeline (embedding generation, vector search, reranking, and text generation) in one SQL query", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- SDKs for Python, JavaScript, and Rust (more languages planned)", "raw": "- SDKs for Python, JavaScript, and Rust (more languages planned)", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Built on PostgreSQL, leveraging pgvector and pgml", "raw": "- Built on PostgreSQL, leveraging pgvector and pgml", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Open-source, with support for open models", "raw": "- Open-source, with support for open models", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "- Designed for high performance and scalability", "raw": "- Designed for high performance and scalability", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "We're eager to get feedback from the community and welcome contributions. Check out our GitHub repo for more details:", "raw": "We're eager to get feedback from the community and welcome contributions. Check out our GitHub repo for more details:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/postgresml/korvus", "resource": null, "url": null, "href": "https://github.com/postgresml/korvus", "user": null, "lang": null, "code": null, "label": null } ]
Korvus: Single-query RAG with Postgres Hey folks! We built Korvus, an open-source RAG (Retrieval-Augmented Generation) pipeline that consolidates the entire RAG workflow - from embedding generation to text generation - into a single SQL query, significantly reducing architectural complexity and latency. https://github.com/postgresml/korvus Here's some of the highlights: - Full RAG pipeline (embedding generation, vector search, reranking, and text generation) in one SQL query - SDKs for Python, JavaScript, and Rust (more languages planned) - Built on PostgreSQL, leveraging pgvector and pgml - Open-source, with support for open models - Designed for high performance and scalability We're eager to get feedback from the community and welcome contributions. Check out our GitHub repo for more details: https://github.com/postgresml/korvus
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2024-07-10T20:54:49.000Z
2024-07-15T23:12:30.529Z
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/posts/cassandrapgml/514389095252830
825
1
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I am working to add automatic speech bubbles to the AI Comic Factory! This is not finished yet - there are a few things to tweak - but so far results are pretty promising! (it's available through a "beta" toggle)
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2024-07-10T17:46:02.000Z
2024-09-12T06:15:35.518Z
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/posts/jbilcke-hf/190522240303219
24,003
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Continuing the series of datasets with code from Russian platforms: GitFlic Code Dataset - https://huggingface.co/datasets/nyuuzyou/gitflic-code. ๐Ÿ“Š Dataset highlights: 60 GB of unique code extracted from over 967 GB of analyzed data 12,527 repositories 5,983,358 unique code files 692 different file types Multilingual: various programming languages ๐ŸŒ Sourced from GitFlic, the first Russian service for storing and working with source code, based on the Git version control system.
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2024-07-10T17:13:50.000Z
2024-07-10T17:14:37.657Z
[]
/posts/nyuuzyou/198825818195694
731
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[ { "type": "text", "value": "Researchers from Auburn University and the University of Alberta have explored the limitations of Vision Language Models (VLMs) in their recently published paper titled \"Vision language models are blind.\" (", "raw": "Researchers from Auburn University and the University of Alberta have explored the limitations of Vision Language Models (VLMs) in their recently published paper titled \"Vision language models are blind.\" (", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2407.06581", "resource": { "type": "paper", "id": "2407.06581", "discussionNum": null }, "url": "https://huggingface.co/papers/2407.06581", "href": null, "user": null, "lang": null, "code": null, "label": "Vision language models are blind (2407.06581)" }, { "type": "text", "value": ")", "raw": ")", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Key Findings:๐Ÿ”", "raw": "Key Findings:๐Ÿ”", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "VLMs, including GPT-4o, Gemini-1.5 Pro, Claude-3 Sonnet, and Claude-3.5 Sonnet, struggle with basic visual tasks.", "raw": "VLMs, including GPT-4o, Gemini-1.5 Pro, Claude-3 Sonnet, and Claude-3.5 Sonnet, struggle with basic visual tasks.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Tasks such as identifying where lines intersect or counting basic shapes are challenging for these models.", "raw": "Tasks such as identifying where lines intersect or counting basic shapes are challenging for these models.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The authors noted, \"The shockingly poor performance of four state-of-the-art VLMs suggests their vision is, at best, like of a person with myopia seeing fine details as blurry, and at worst, like an intelligent person that is blind making educated guesses\"โ€‹(Vision Language Models Are Blind; 2024)โ€‹.", "raw": "The authors noted, \"The shockingly poor performance of four state-of-the-art VLMs suggests their vision is, at best, like of a person with myopia seeing fine details as blurry, and at worst, like an intelligent person that is blind making educated guesses\"โ€‹(Vision Language Models Are Blind; 2024)โ€‹.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Human-like Myopia? ๐Ÿ‘“", "raw": "Human-like Myopia? ๐Ÿ‘“", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "VLMs may have a blind spot similar to human myopia.", "raw": "VLMs may have a blind spot similar to human myopia.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This limitation makes it difficult for VLMs to perceive details.", "raw": "This limitation makes it difficult for VLMs to perceive details.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Suggests a potential parallel between human and machine vision limitations.", "raw": "Suggests a potential parallel between human and machine vision limitations.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Technical Details: ๐Ÿ”ง", "raw": "Technical Details: ๐Ÿ”ง", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The researchers created a new benchmark called BlindTest.", "raw": "The researchers created a new benchmark called BlindTest.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "BlindTest consists of simple visual tasks to evaluate VLMs low-level vision capabilities.", "raw": "BlindTest consists of simple visual tasks to evaluate VLMs low-level vision capabilities.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Four VLMs were assessed using BlindTest.", "raw": "Four VLMs were assessed using BlindTest.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Many shortcomings were revealed in the models ability to process basic visual information.", "raw": "Many shortcomings were revealed in the models ability to process basic visual information.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Learn More: ๐Ÿ–ผ๏ธ", "raw": "Learn More: ๐Ÿ–ผ๏ธ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "For a deeper dive into this research, check out the project page: ", "raw": "For a deeper dive into this research, check out the project page: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://vlmsareblind.github.io/", "resource": null, "url": null, "href": "https://vlmsareblind.github.io/", "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null } ]
Researchers from Auburn University and the University of Alberta have explored the limitations of Vision Language Models (VLMs) in their recently published paper titled "Vision language models are blind." (https://huggingface.co/papers/2407.06581) Key Findings:๐Ÿ” VLMs, including GPT-4o, Gemini-1.5 Pro, Claude-3 Sonnet, and Claude-3.5 Sonnet, struggle with basic visual tasks. Tasks such as identifying where lines intersect or counting basic shapes are challenging for these models. The authors noted, "The shockingly poor performance of four state-of-the-art VLMs suggests their vision is, at best, like of a person with myopia seeing fine details as blurry, and at worst, like an intelligent person that is blind making educated guesses"โ€‹(Vision Language Models Are Blind; 2024)โ€‹. Human-like Myopia? ๐Ÿ‘“ VLMs may have a blind spot similar to human myopia. This limitation makes it difficult for VLMs to perceive details. Suggests a potential parallel between human and machine vision limitations. Technical Details: ๐Ÿ”ง The researchers created a new benchmark called BlindTest. BlindTest consists of simple visual tasks to evaluate VLMs low-level vision capabilities. Four VLMs were assessed using BlindTest. Many shortcomings were revealed in the models ability to process basic visual information. Learn More: ๐Ÿ–ผ๏ธ For a deeper dive into this research, check out the project page: https://vlmsareblind.github.io/
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2024-07-10T16:24:46.000Z
2024-09-18T14:28:48.611Z
[]
/posts/Taylor658/569087660586262
805
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[ { "type": "text", "value": "Introducing Whisper Timestamped: Multilingual speech recognition with word-level timestamps, running 100% locally in your browser thanks to ๐Ÿค— Transformers.js! Check it out!", "raw": "Introducing Whisper Timestamped: Multilingual speech recognition with word-level timestamps, running 100% locally in your browser thanks to ๐Ÿค— Transformers.js! Check it out!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ‘‰ ", "raw": "๐Ÿ‘‰ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Xenova/whisper-word-level-timestamps", "resource": { "type": "space", "id": "Xenova/whisper-word-level-timestamps", "discussionNum": null }, "url": "https://huggingface.co/spaces/Xenova/whisper-word-level-timestamps", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": " ๐Ÿ‘ˆ", "raw": " ๐Ÿ‘ˆ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This unlocks a world of possibilities for in-browser video editing! ๐Ÿคฏ What will you build? ๐Ÿ˜", "raw": "This unlocks a world of possibilities for in-browser video editing! ๐Ÿคฏ What will you build? ๐Ÿ˜", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Source code: ", "raw": "Source code: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://github.com/xenova/transformers.js/tree/v3/examples/whisper-word-timestamps", "resource": null, "url": null, "href": "https://github.com/xenova/transformers.js/tree/v3/examples/whisper-word-timestamps", "user": null, "lang": null, "code": null, "label": null } ]
Introducing Whisper Timestamped: Multilingual speech recognition with word-level timestamps, running 100% locally in your browser thanks to ๐Ÿค— Transformers.js! Check it out! ๐Ÿ‘‰ https://huggingface.co/spaces/Xenova/whisper-word-level-timestamps ๐Ÿ‘ˆ This unlocks a world of possibilities for in-browser video editing! ๐Ÿคฏ What will you build? ๐Ÿ˜ Source code: https://github.com/xenova/transformers.js/tree/v3/examples/whisper-word-timestamps
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2024-07-10T16:19:35.000Z
2024-07-18T00:53:47.196Z
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/posts/Xenova/386072637398041
6,696
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564483851260057
[ { "type": "text", "value": "Ghost 8B Beta is a large language model developed with goals that include excellent multilingual support, superior knowledge capabilities, and cost-effectiveness. The model comes in two context length versions, 8k and 128k, along with multilingual function tools support by default.", "raw": "Ghost 8B Beta is a large language model developed with goals that include excellent multilingual support, superior knowledge capabilities, and cost-effectiveness. The model comes in two context length versions, 8k and 128k, along with multilingual function tools support by default.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "* The languages supported are ๐Ÿ‡บ๐Ÿ‡ธ English, ๐Ÿ‡ซ๐Ÿ‡ท French, ๐Ÿ‡ฎ๐Ÿ‡น Italian, ๐Ÿ‡ช๐Ÿ‡ธ Spanish, ๐Ÿ‡ต๐Ÿ‡น Portuguese, ๐Ÿ‡ฉ๐Ÿ‡ช German, ๐Ÿ‡ป๐Ÿ‡ณ Vietnamese, ๐Ÿ‡ฐ๐Ÿ‡ท Korean and ๐Ÿ‡จ๐Ÿ‡ณ Chinese.", "raw": "* The languages supported are ๐Ÿ‡บ๐Ÿ‡ธ English, ๐Ÿ‡ซ๐Ÿ‡ท French, ๐Ÿ‡ฎ๐Ÿ‡น Italian, ๐Ÿ‡ช๐Ÿ‡ธ Spanish, ๐Ÿ‡ต๐Ÿ‡น Portuguese, ๐Ÿ‡ฉ๐Ÿ‡ช German, ๐Ÿ‡ป๐Ÿ‡ณ Vietnamese, ๐Ÿ‡ฐ๐Ÿ‡ท Korean and ๐Ÿ‡จ๐Ÿ‡ณ Chinese.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "* ๐Ÿ‘จโ€๐Ÿ’ป Try on Spaces: ", "raw": "* ๐Ÿ‘จโ€๐Ÿ’ป Try on Spaces: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k", "resource": { "type": "space", "id": "lamhieu/ghost-8b-beta-8k", "discussionNum": null }, "url": "https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k", "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "* ๐Ÿ“‹ Official website: ", "raw": "* ๐Ÿ“‹ Official website: ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "link", "value": null, "raw": "https://ghost-x.org/docs/models/ghost-8b-beta", "resource": null, "url": null, "href": "https://ghost-x.org/docs/models/ghost-8b-beta", "user": null, "lang": null, "code": null, "label": null } ]
Ghost 8B Beta is a large language model developed with goals that include excellent multilingual support, superior knowledge capabilities, and cost-effectiveness. The model comes in two context length versions, 8k and 128k, along with multilingual function tools support by default. * The languages supported are ๐Ÿ‡บ๐Ÿ‡ธ English, ๐Ÿ‡ซ๐Ÿ‡ท French, ๐Ÿ‡ฎ๐Ÿ‡น Italian, ๐Ÿ‡ช๐Ÿ‡ธ Spanish, ๐Ÿ‡ต๐Ÿ‡น Portuguese, ๐Ÿ‡ฉ๐Ÿ‡ช German, ๐Ÿ‡ป๐Ÿ‡ณ Vietnamese, ๐Ÿ‡ฐ๐Ÿ‡ท Korean and ๐Ÿ‡จ๐Ÿ‡ณ Chinese. * ๐Ÿ‘จโ€๐Ÿ’ป Try on Spaces: https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k * ๐Ÿ“‹ Official website: https://ghost-x.org/docs/models/ghost-8b-beta
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2024-07-10T15:51:55.000Z
2024-07-16T14:13:59.173Z
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/posts/lamhieu/564483851260057
1,509
1
373850915662298
[ { "type": "text", "value": "๐๐ž๐ฐ ๐๐ž๐œ๐จ๐๐ข๐ง๐  ๐ญ๐ž๐œ๐ก๐ง๐ข๐ช๐ฎ๐ž ๐ข๐ง ๐ญ๐ซ๐š๐ง๐ฌ๐Ÿ๐จ๐ซ๐ฆ๐ž๐ซ๐ฌ ๐ฌ๐ข๐ ๐ง๐ข๐Ÿ๐ข๐œ๐š๐ง๐ญ๐ฅ๐ฒ ๐ซ๐ž๐๐ฎ๐œ๐ž๐ฌ ๐ก๐š๐ฅ๐ฅ๐ฎ๐œ๐ข๐ง๐š๐ญ๐ข๐จ๐ง๐ฌ ๐Ÿ‘", "raw": "๐๐ž๐ฐ ๐๐ž๐œ๐จ๐๐ข๐ง๐  ๐ญ๐ž๐œ๐ก๐ง๐ข๐ช๐ฎ๐ž ๐ข๐ง ๐ญ๐ซ๐š๐ง๐ฌ๐Ÿ๐จ๐ซ๐ฆ๐ž๐ซ๐ฌ ๐ฌ๐ข๐ ๐ง๐ข๐Ÿ๐ข๐œ๐š๐ง๐ญ๐ฅ๐ฒ ๐ซ๐ž๐๐ฎ๐œ๐ž๐ฌ ๐ก๐š๐ฅ๐ฅ๐ฎ๐œ๐ข๐ง๐š๐ญ๐ข๐จ๐ง๐ฌ ๐Ÿ‘", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "DoLa decoding, which made a conference paper at ICLR '24, has just been merged in Transformers by ", "raw": "DoLa decoding, which made a conference paper at ICLR '24, has just been merged in Transformers by ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "mention", "value": null, "raw": "@joaogante", "resource": null, "url": null, "href": null, "user": "joaogante", "lang": null, "code": null, "label": null }, { "type": "text", "value": " and Yung-Sung Chuang.", "raw": " and Yung-Sung Chuang.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This new decoding method is simple yet extremely impressive!", "raw": "This new decoding method is simple yet extremely impressive!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Reminder: Decoder LLMs (the GPT kind of LLM, the most common one) generate their outputs one token at a time: at each step, given a current text, they compute a logit for each token in their vocabulary that should represent the probability of this token coming next.", "raw": "Reminder: Decoder LLMs (the GPT kind of LLM, the most common one) generate their outputs one token at a time: at each step, given a current text, they compute a logit for each token in their vocabulary that should represent the probability of this token coming next.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Then they either pick the highest logit token (greedy decoding) or sample one with a probability defined by the logits (sampling).", "raw": "Then they either pick the highest logit token (greedy decoding) or sample one with a probability defined by the logits (sampling).", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "The authors of DoLa wanted to improve that simple method. ", "raw": "The authors of DoLa wanted to improve that simple method. ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "They knew this established fact that transformer LMs encode low-level info (like base syntax) in early layers and more high-level info like knowledge in the later layers.", "raw": "They knew this established fact that transformer LMs encode low-level info (like base syntax) in early layers and more high-level info like knowledge in the later layers.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿ’ก This gave them their key idea: During decoding, rather than picking the token with the highest logit, ๐˜„๐—ต๐˜† ๐—ป๐—ผ๐˜ ๐—ฝ๐—ถ๐—ฐ๐—ธ ๐˜๐—ต๐—ฒ ๐˜๐—ผ๐—ธ๐—ฒ๐—ป ๐˜„๐—ถ๐˜๐—ต ๐˜๐—ต๐—ฒ ๐—บ๐—ผ๐˜€๐˜ ๐—ถ๐—บ๐—ฝ๐—ฟ๐—ฒ๐˜€๐˜€๐—ถ๐˜ƒ๐—ฒ ๐—ถ๐—ป๐—ฐ๐—ฟ๐—ฒ๐—ฎ๐˜€๐—ฒ ๐—ถ๐—ป ๐—น๐—ผ๐—ด๐—ถ๐˜ ๐—ฎ๐—ฐ๐—ฟ๐—ผ๐˜€๐˜€ ๐—น๐—ฎ๐˜†๐—ฒ๐—ฟ๐˜€?", "raw": "๐Ÿ’ก This gave them their key idea: During decoding, rather than picking the token with the highest logit, ๐˜„๐—ต๐˜† ๐—ป๐—ผ๐˜ ๐—ฝ๐—ถ๐—ฐ๐—ธ ๐˜๐—ต๐—ฒ ๐˜๐—ผ๐—ธ๐—ฒ๐—ป ๐˜„๐—ถ๐˜๐—ต ๐˜๐—ต๐—ฒ ๐—บ๐—ผ๐˜€๐˜ ๐—ถ๐—บ๐—ฝ๐—ฟ๐—ฒ๐˜€๐˜€๐—ถ๐˜ƒ๐—ฒ ๐—ถ๐—ป๐—ฐ๐—ฟ๐—ฒ๐—ฎ๐˜€๐—ฒ ๐—ถ๐—ป ๐—น๐—ผ๐—ด๐—ถ๐˜ ๐—ฎ๐—ฐ๐—ฟ๐—ผ๐˜€๐˜€ ๐—น๐—ฎ๐˜†๐—ฒ๐—ฟ๐˜€?", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "This gives impressive results:", "raw": "This gives impressive results:", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ ๐Ÿฑ% - ๐Ÿฎ๐Ÿฌ% ๐—ฏ๐—ฎ๐˜€๐—ฒ ๐—ฝ๐—ผ๐—ถ๐—ป๐˜๐˜€ ๐—ถ๐—ป๐—ฐ๐—ฟ๐—ฒ๐—ฎ๐˜€๐—ฒ ๐—ฎ๐—ฐ๐—ฟ๐—ผ๐˜€๐˜€ ๐˜๐—ต๐—ฒ ๐—ฏ๐—ฒ๐—ป๐—ฐ๐—ต๐—บ๐—ฎ๐—ฟ๐—ธ๐˜€", "raw": "๐Ÿš€ ๐Ÿฑ% - ๐Ÿฎ๐Ÿฌ% ๐—ฏ๐—ฎ๐˜€๐—ฒ ๐—ฝ๐—ผ๐—ถ๐—ป๐˜๐˜€ ๐—ถ๐—ป๐—ฐ๐—ฟ๐—ฒ๐—ฎ๐˜€๐—ฒ ๐—ฎ๐—ฐ๐—ฟ๐—ผ๐˜€๐˜€ ๐˜๐—ต๐—ฒ ๐—ฏ๐—ฒ๐—ป๐—ฐ๐—ต๐—บ๐—ฎ๐—ฟ๐—ธ๐˜€", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿš€ For instance on TruthfulQA / Open-ended, across all model sizes the increase in truthfulness is 14 base points, which is ๐—ฎ๐—ฟ๐—ผ๐˜‚๐—ป๐—ฑ ๐Ÿฐ๐Ÿฌ% ๐—ถ๐—บ๐—ฝ๐—ฟ๐—ผ๐˜ƒ๐—ฒ๐—บ๐—ฒ๐—ป๐˜ ๐—ฐ๐—ผ๐—บ๐—ฝ๐—ฎ๐—ฟ๐—ฒ๐—ฑ ๐˜๐—ผ ๐˜€๐˜๐—ฎ๐—ป๐—ฑ๐—ฎ๐—ฟ๐—ฑ ๐—ฑ๐—ฒ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด!", "raw": "๐Ÿš€ For instance on TruthfulQA / Open-ended, across all model sizes the increase in truthfulness is 14 base points, which is ๐—ฎ๐—ฟ๐—ผ๐˜‚๐—ป๐—ฑ ๐Ÿฐ๐Ÿฌ% ๐—ถ๐—บ๐—ฝ๐—ฟ๐—ผ๐˜ƒ๐—ฒ๐—บ๐—ฒ๐—ป๐˜ ๐—ฐ๐—ผ๐—บ๐—ฝ๐—ฎ๐—ฟ๐—ฒ๐—ฑ ๐˜๐—ผ ๐˜€๐˜๐—ฎ๐—ป๐—ฑ๐—ฎ๐—ฟ๐—ฑ ๐—ฑ๐—ฒ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด!", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "๐Ÿค” Wouldn't decoding take longer because of this added contrasting step? ๐Ÿ‘‰ ๐—ง๐—ต๐—ฒ ๐—ฟ๐˜‚๐—ป๐˜๐—ถ๐—บ๐—ฒ ๐—ถ๐—ป๐—ฐ๐—ฟ๐—ฒ๐—ฎ๐˜€๐—ฒ ๐—ถ๐˜€ ๐—ป๐—ฒ๐—ด๐—น๐—ถ๐—ด๐—ถ๐—ฏ๐—น๐—ฒ, ๐Ÿญ ๐˜๐—ผ ๐Ÿด% ๐—ผ๐—ป๐—น๐˜†.", "raw": "๐Ÿค” Wouldn't decoding take longer because of this added contrasting step? ๐Ÿ‘‰ ๐—ง๐—ต๐—ฒ ๐—ฟ๐˜‚๐—ป๐˜๐—ถ๐—บ๐—ฒ ๐—ถ๐—ป๐—ฐ๐—ฟ๐—ฒ๐—ฎ๐˜€๐—ฒ ๐—ถ๐˜€ ๐—ป๐—ฒ๐—ด๐—น๐—ถ๐—ด๐—ถ๐—ฏ๐—น๐—ฒ, ๐Ÿญ ๐˜๐—ผ ๐Ÿด% ๐—ผ๐—ป๐—น๐˜†.", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "new_line", "value": null, "raw": "\n", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "text", "value": "Paper added to my collection ๐Ÿ‘‰ ", "raw": "Paper added to my collection ๐Ÿ‘‰ ", "resource": null, "url": null, "href": null, "user": null, "lang": null, "code": null, "label": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/m-ric/optimization-mechanics-661d543a5fc6ca1dc84284a0", "resource": { "type": "collection", "id": "m-ric/optimization-mechanics-661d543a5fc6ca1dc84284a0", "discussionNum": null }, "url": "https://huggingface.co/collections/m-ric/optimization-mechanics-661d543a5fc6ca1dc84284a0", "href": null, "user": null, "lang": null, "code": null, "label": null } ]
๐๐ž๐ฐ ๐๐ž๐œ๐จ๐๐ข๐ง๐  ๐ญ๐ž๐œ๐ก๐ง๐ข๐ช๐ฎ๐ž ๐ข๐ง ๐ญ๐ซ๐š๐ง๐ฌ๐Ÿ๐จ๐ซ๐ฆ๐ž๐ซ๐ฌ ๐ฌ๐ข๐ ๐ง๐ข๐Ÿ๐ข๐œ๐š๐ง๐ญ๐ฅ๐ฒ ๐ซ๐ž๐๐ฎ๐œ๐ž๐ฌ ๐ก๐š๐ฅ๐ฅ๐ฎ๐œ๐ข๐ง๐š๐ญ๐ข๐จ๐ง๐ฌ ๐Ÿ‘ DoLa decoding, which made a conference paper at ICLR '24, has just been merged in Transformers by @joaogante and Yung-Sung Chuang. This new decoding method is simple yet extremely impressive! Reminder: Decoder LLMs (the GPT kind of LLM, the most common one) generate their outputs one token at a time: at each step, given a current text, they compute a logit for each token in their vocabulary that should represent the probability of this token coming next. Then they either pick the highest logit token (greedy decoding) or sample one with a probability defined by the logits (sampling). The authors of DoLa wanted to improve that simple method. They knew this established fact that transformer LMs encode low-level info (like base syntax) in early layers and more high-level info like knowledge in the later layers. ๐Ÿ’ก This gave them their key idea: During decoding, rather than picking the token with the highest logit, ๐˜„๐—ต๐˜† ๐—ป๐—ผ๐˜ ๐—ฝ๐—ถ๐—ฐ๐—ธ ๐˜๐—ต๐—ฒ ๐˜๐—ผ๐—ธ๐—ฒ๐—ป ๐˜„๐—ถ๐˜๐—ต ๐˜๐—ต๐—ฒ ๐—บ๐—ผ๐˜€๐˜ ๐—ถ๐—บ๐—ฝ๐—ฟ๐—ฒ๐˜€๐˜€๐—ถ๐˜ƒ๐—ฒ ๐—ถ๐—ป๐—ฐ๐—ฟ๐—ฒ๐—ฎ๐˜€๐—ฒ ๐—ถ๐—ป ๐—น๐—ผ๐—ด๐—ถ๐˜ ๐—ฎ๐—ฐ๐—ฟ๐—ผ๐˜€๐˜€ ๐—น๐—ฎ๐˜†๐—ฒ๐—ฟ๐˜€? This gives impressive results: ๐Ÿš€ ๐Ÿฑ% - ๐Ÿฎ๐Ÿฌ% ๐—ฏ๐—ฎ๐˜€๐—ฒ ๐—ฝ๐—ผ๐—ถ๐—ป๐˜๐˜€ ๐—ถ๐—ป๐—ฐ๐—ฟ๐—ฒ๐—ฎ๐˜€๐—ฒ ๐—ฎ๐—ฐ๐—ฟ๐—ผ๐˜€๐˜€ ๐˜๐—ต๐—ฒ ๐—ฏ๐—ฒ๐—ป๐—ฐ๐—ต๐—บ๐—ฎ๐—ฟ๐—ธ๐˜€ ๐Ÿš€ For instance on TruthfulQA / Open-ended, across all model sizes the increase in truthfulness is 14 base points, which is ๐—ฎ๐—ฟ๐—ผ๐˜‚๐—ป๐—ฑ ๐Ÿฐ๐Ÿฌ% ๐—ถ๐—บ๐—ฝ๐—ฟ๐—ผ๐˜ƒ๐—ฒ๐—บ๐—ฒ๐—ป๐˜ ๐—ฐ๐—ผ๐—บ๐—ฝ๐—ฎ๐—ฟ๐—ฒ๐—ฑ ๐˜๐—ผ ๐˜€๐˜๐—ฎ๐—ป๐—ฑ๐—ฎ๐—ฟ๐—ฑ ๐—ฑ๐—ฒ๐—ฐ๐—ผ๐—ฑ๐—ถ๐—ป๐—ด! ๐Ÿค” Wouldn't decoding take longer because of this added contrasting step? ๐Ÿ‘‰ ๐—ง๐—ต๐—ฒ ๐—ฟ๐˜‚๐—ป๐˜๐—ถ๐—บ๐—ฒ ๐—ถ๐—ป๐—ฐ๐—ฟ๐—ฒ๐—ฎ๐˜€๐—ฒ ๐—ถ๐˜€ ๐—ป๐—ฒ๐—ด๐—น๐—ถ๐—ด๐—ถ๐—ฏ๐—น๐—ฒ, ๐Ÿญ ๐˜๐—ผ ๐Ÿด% ๐—ผ๐—ป๐—น๐˜†. Paper added to my collection ๐Ÿ‘‰ https://huggingface.co/collections/m-ric/optimization-mechanics-661d543a5fc6ca1dc84284a0
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2024-07-10T13:56:16.000Z
2024-07-12T17:09:48.803Z
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/posts/m-ric/373850915662298
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