Datasets:

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maxiw

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hf-posts

like 18

[ { "type": "text", "value": "\"The Case for Co-Designing Model Architectures with Hardware\"", "raw": "\"The Case for Co-Designing Model Architectures with Hardware\"", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is a long overdue paper that we have started discussing back when training BLOOM-176.", "raw": "This is a long overdue paper that we have started discussing back when training BLOOM-176.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Basically this paper tells you how to design your model's dimensions for an optimal training throughput.", "raw": "Basically this paper tells you how to design your model's dimensions for an optimal training throughput.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Fantastic!", "raw": "Fantastic!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Yours truly contributed the SwiGLU section ;)", "raw": "Yours truly contributed the SwiGLU section ;)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://twitter.com/QuentinAnthon15/status/1752393989813375119", "href": "https://twitter.com/QuentinAnthon15/status/1752393989813375119", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2401.14489", "href": "https://arxiv.org/abs/2401.14489", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "So impressed with the speed and accuracy of ", "raw": "So impressed with the speed and accuracy of ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/vikhyatk/moondream1", "href": null, "resource": { "type": "space", "id": "vikhyatk/moondream1", "discussionNum": null }, "url": "https://huggingface.co/spaces/vikhyatk/moondream1", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " by ", "raw": " by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@vikhyatk", "href": null, "resource": null, "url": null, "code": null, "user": "vikhyatk", "label": null, "lang": null }, { "type": "text", "value": " (especially the last answer 😝😝😝). ", "raw": " (especially the last answer 😝😝😝). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Open multi-modal models have gone a long way!", "raw": "Open multi-modal models have gone a long way!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/vikhyatk/moondream1", "href": null, "resource": { "type": "model", "id": "vikhyatk/moondream1", "discussionNum": null }, "url": "https://huggingface.co/vikhyatk/moondream1", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Delighted to share a course I've learned a ton from about getting better outputs from LLMs", "raw": "Delighted to share a course I've learned a ton from about getting better outputs from LLMs", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.wandb.courses/courses/steering-language-models", "href": "https://www.wandb.courses/courses/steering-language-models", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We released it last Thursday (free) and at just 30 minutes of content total, its very information-dense with non-stop learnings covering important concepts around LLM validation, making your approach to LLM prompting more pythonic and quickly covers a basic RAG application at the end.", "raw": "We released it last Thursday (free) and at just 30 minutes of content total, its very information-dense with non-stop learnings covering important concepts around LLM validation, making your approach to LLM prompting more pythonic and quickly covers a basic RAG application at the end.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Would love to hear what ye think!", "raw": "Would love to hear what ye think!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Quick build of the day: LCM Supa Fast Image Variation", "raw": "Quick build of the day: LCM Supa Fast Image Variation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "—", "raw": "—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We take the opportunity to combine moondream1 vision and LCM SDXL fast abilities to generate a variation from the subject of the image input. ", "raw": "We take the opportunity to combine moondream1 vision and LCM SDXL fast abilities to generate a variation from the subject of the image input. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "All that thanks to gradio APIs 🤗", "raw": "All that thanks to gradio APIs 🤗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Try the space: ", "raw": "Try the space: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/fffiloni/lcm-img-variations", "href": "https://huggingface.co/spaces/fffiloni/lcm-img-variations", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "InternLM-XComposer2", "raw": "InternLM-XComposer2", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model", "raw": "Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "paper page: ", "raw": "paper page: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.16420", "href": null, "resource": { "type": "paper", "id": "2401.16420", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.16420", "code": null, "user": null, "label": "InternLM-XComposer2: Mastering Free-form Text-Image Composition and\n Comprehension in Vision-Language Large Model (2401.16420)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Experimental results demonstrate the superiority of InternLM-XComposer2 based on InternLM2-7B in producing high-quality long-text multi-modal content and its exceptional vision-language understanding performance across various benchmarks, where it not only significantly outperforms existing multimodal models but also matches or even surpasses GPT-4V and Gemini Pro in certain assessments.", "raw": "Experimental results demonstrate the superiority of InternLM-XComposer2 based on InternLM2-7B in producing high-quality long-text multi-modal content and its exceptional vision-language understanding performance across various benchmarks, where it not only significantly outperforms existing multimodal models but also matches or even surpasses GPT-4V and Gemini Pro in certain assessments.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🔍 Today's pick in Interpretability & Analysis of LMs: Black-Box Access is Insufficient for Rigorous AI Audits by ", "raw": "🔍 Today's pick in Interpretability & Analysis of LMs: Black-Box Access is Insufficient for Rigorous AI Audits by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@stecas", "href": null, "resource": null, "url": null, "code": null, "user": "stecas", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@carsonezell", "href": null, "resource": null, "url": null, "code": null, "user": "carsonezell", "label": null, "lang": null }, { "type": "text", "value": " et al.", "raw": " et al.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Audits conducted on AI systems can identify potential risks and ensure their compliance to safety requirements. Authors categorise audits based on the access to model-related resources (black, grey, white and out-of-the box) and highlight how levels of transparency on audited AI system enable broader and more effective auditing procedures. Technical, physical, and legal safeguards for performing audits are also introduced to ensure minimal security risks for audited companies. Authors conclude that transparency on the type of auditors’ access and methods is a pre-requisite to correctly interpret audit results, and white- and outside-the-box access allow for substantially more scrutiny than black-box access alone.", "raw": "Audits conducted on AI systems can identify potential risks and ensure their compliance to safety requirements. Authors categorise audits based on the access to model-related resources (black, grey, white and out-of-the box) and highlight how levels of transparency on audited AI system enable broader and more effective auditing procedures. Technical, physical, and legal safeguards for performing audits are also introduced to ensure minimal security risks for audited companies. Authors conclude that transparency on the type of auditors’ access and methods is a pre-requisite to correctly interpret audit results, and white- and outside-the-box access allow for substantially more scrutiny than black-box access alone.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Paper: ", "raw": "📄 Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.14446", "href": null, "resource": { "type": "paper", "id": "2401.14446", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.14446", "code": null, "user": null, "label": "Black-Box Access is Insufficient for Rigorous AI Audits (2401.14446)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🔍 Further readings:", "raw": "🔍 Further readings:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄Taxonomy of AI system access: ", "raw": "📄Taxonomy of AI system access: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://bit.ly/struct-access", "href": "https://bit.ly/struct-access", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💻An API for transparent science on Black-box AI (NNsight): ", "raw": "💻An API for transparent science on Black-box AI (NNsight): ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://nnsight.net/about", "href": "https://nnsight.net/about", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "The most interesting LLM benchmark I've seen so far... reminder that there's lots of characterization of LLMs still yet to do.", "raw": "The most interesting LLM benchmark I've seen so far... reminder that there's lots of characterization of LLMs still yet to do.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2312.06281", "href": null, "resource": { "type": "paper", "id": "2312.06281", "discussionNum": null }, "url": "https://huggingface.co/papers/2312.06281", "code": null, "user": null, "label": "EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models (2312.06281)", "lang": null } ]

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[ { "type": "text", "value": "Fascinating paper by Rand shows that there is no statistically significant difference between using LLMs or regular internet to craft operational plans for bioweapons!", "raw": "Fascinating paper by Rand shows that there is no statistically significant difference between using LLMs or regular internet to craft operational plans for bioweapons!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is the first paper that actually studies the impact of AI on bioweapons from an operational perspective and looks at the big question: is AI any better than just using public data on the Internet?", "raw": "This is the first paper that actually studies the impact of AI on bioweapons from an operational perspective and looks at the big question: is AI any better than just using public data on the Internet?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "As most of the data is most likely out there, an LLM would just be a more efficient tool to come up with the relevant information, but it seems that its impact is limited.", "raw": "As most of the data is most likely out there, an LLM would just be a more efficient tool to come up with the relevant information, but it seems that its impact is limited.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.rand.org/pubs/research_reports/RRA2977-2.html", "href": "https://www.rand.org/pubs/research_reports/RRA2977-2.html", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "SliceGPT: Compress Large Language Models by Deleting Rows and Columns", "raw": "SliceGPT: Compress Large Language Models by Deleting Rows and Columns", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.15024", "href": null, "resource": { "type": "paper", "id": "2401.15024", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.15024", "code": null, "user": null, "label": "SliceGPT: Compress Large Language Models by Deleting Rows and Columns (2401.15024)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Large language models have become the cornerstone of natural language processing, but their use comes with substantial costs in terms of compute and memory resources. Sparsification provides a solution to alleviate these resource constraints, and recent works have shown that trained models can be sparsified post-hoc. Existing sparsification techniques face challenges as they need additional data structures and offer constrained speedup with current hardware. In this paper we present SliceGPT, a new post-training sparsification scheme which replaces each weight matrix with a smaller (dense) matrix, reducing the embedding dimension of the network. Through extensive experimentation, we show that SliceGPT can remove up to 25% of the model parameters (including embeddings) for LLAMA2-70B, OPT 66B and Phi-2 models while maintaining 99%, 99% and 90% zero-shot task performance of the dense model respectively. Our sliced models run on fewer GPUs and run faster without any additional code optimization: on 24GB consumer GPUs we reduce the total compute for inference on LLAMA2-70B to 64% of that of the dense model; on 40GB A100 GPUs we reduce it to 66%. We offer a new insight, computational invariance in transformer networks, which enables SliceGPT and we hope it will inspire and enable future avenues to reduce memory and computation demands for pre-trained models.", "raw": "Large language models have become the cornerstone of natural language processing, but their use comes with substantial costs in terms of compute and memory resources. Sparsification provides a solution to alleviate these resource constraints, and recent works have shown that trained models can be sparsified post-hoc. Existing sparsification techniques face challenges as they need additional data structures and offer constrained speedup with current hardware. In this paper we present SliceGPT, a new post-training sparsification scheme which replaces each weight matrix with a smaller (dense) matrix, reducing the embedding dimension of the network. Through extensive experimentation, we show that SliceGPT can remove up to 25% of the model parameters (including embeddings) for LLAMA2-70B, OPT 66B and Phi-2 models while maintaining 99%, 99% and 90% zero-shot task performance of the dense model respectively. Our sliced models run on fewer GPUs and run faster without any additional code optimization: on 24GB consumer GPUs we reduce the total compute for inference on LLAMA2-70B to 64% of that of the dense model; on 40GB A100 GPUs we reduce it to 66%. We offer a new insight, computational invariance in transformer networks, which enables SliceGPT and we hope it will inspire and enable future avenues to reduce memory and computation demands for pre-trained models.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "With the Google announcement last week, I think we're now officially the only AI startup out there who has commercial collaborations with all the major cloud providers (AWS, GCP, Azure) and hardware providers (Nvidia, AMD, Intel, Qualcomm,...), making our vision of being the independent and agnostic platform for all AI builders truer than ever! ", "raw": "With the Google announcement last week, I think we're now officially the only AI startup out there who has commercial collaborations with all the major cloud providers (AWS, GCP, Azure) and hardware providers (Nvidia, AMD, Intel, Qualcomm,...), making our vision of being the independent and agnostic platform for all AI builders truer than ever! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Let's go!", "raw": "Let's go!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Confidence * may be * all you need.", "raw": "Confidence * may be * all you need.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "A simple average of the log probabilities of the output tokens from an LLM might be all it takes to tell if the model is hallucinating.🫨", "raw": "A simple average of the log probabilities of the output tokens from an LLM might be all it takes to tell if the model is hallucinating.🫨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The idea is that if a model is not confident (low output token probabilities), the model may be inventing random stuff.", "raw": "The idea is that if a model is not confident (low output token probabilities), the model may be inventing random stuff.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In these two papers:", "raw": "In these two papers:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. ", "raw": "1. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://aclanthology.org/2023.eacl-main.75/", "href": "https://aclanthology.org/2023.eacl-main.75/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. ", "raw": "2. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2303.08896", "href": "https://arxiv.org/abs/2303.08896", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The authors claim that this simple method is the best heuristic for detecting hallucinations. The beauty is that it only uses the generated token probabilities, so it can be implemented at inference time ⚡", "raw": "The authors claim that this simple method is the best heuristic for detecting hallucinations. The beauty is that it only uses the generated token probabilities, so it can be implemented at inference time ⚡", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Sentence Transformers v2.3.0 has been released! It includes several bug fixes, enhanced model loading including custom models & no more unnecessary file downloads, improved performance, a powerful loss function, and much more!", "raw": "Sentence Transformers v2.3.0 has been released! It includes several bug fixes, enhanced model loading including custom models & no more unnecessary file downloads, improved performance, a powerful loss function, and much more!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Details:", "raw": "Details:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "⬆ Uploading Models to the Hub with ", "raw": "⬆ Uploading Models to the Hub with ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`save_to_hub`", "href": null, "resource": null, "url": null, "code": "save_to_hub", "user": null, "label": null, "lang": null }, { "type": "text", "value": ".", "raw": ".", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "⬇ Downloading Models from the Hub now downloads only necessary files.", "raw": "⬇ Downloading Models from the Hub now downloads only necessary files.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "⚙ Custom Models (such as ", "raw": "⚙ Custom Models (such as ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", "href": null, "resource": { "type": "model", "id": "jinaai/jina-embeddings-v2-base-de", "discussionNum": null }, "url": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") can now be loaded with ", "raw": ") can now be loaded with ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`trust_remote_code=True`", "href": null, "resource": null, "url": null, "code": "trust_remote_code=True", "user": null, "label": null, "lang": null }, { "type": "text", "value": ".", "raw": ".", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🔍 Models can now be loaded at specific revisions (e.g. commit hashes or git branches).", "raw": "🔍 Models can now be loaded at specific revisions (e.g. commit hashes or git branches).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🖥️ Various device fixes; models will now always operate on the device that you specify.", "raw": "🖥️ Various device fixes; models will now always operate on the device that you specify.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📉 A new \"Cached\" variant of the powerful Multiple Negatives Ranking Loss allows common hardware to reach performance previously only accessible on multi-gpu clusters.", "raw": "📉 A new \"Cached\" variant of the powerful Multiple Negatives Ranking Loss allows common hardware to reach performance previously only accessible on multi-gpu clusters.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🐎 Computation time of Community Detection was decreased significantly (7x speedup at 500k sentences :exploding_head:)", "raw": "🐎 Computation time of Community Detection was decreased significantly (7x speedup at 500k sentences :exploding_head:)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🪶 Removed the now unnecessary \"torchvision\" dependency for a smaller installation.", "raw": "🪶 Removed the now unnecessary \"torchvision\" dependency for a smaller installation.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Check out the full changelog here: ", "raw": "Check out the full changelog here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/UKPLab/sentence-transformers/releases/tag/v2.3.0", "href": "https://github.com/UKPLab/sentence-transformers/releases/tag/v2.3.0", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I'll be working on much more changes in the near future, so expect more exciting updates. If you encounter any issues, or have any questions or feature requests, don't hesitate to open an issue on the repository: ", "raw": "I'll be working on much more changes in the near future, so expect more exciting updates. If you encounter any issues, or have any questions or feature requests, don't hesitate to open an issue on the repository: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/UKPLab/sentence-transformers/issues", "href": "https://github.com/UKPLab/sentence-transformers/issues", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🔥 New LLM leaderboard on the hub: an LLM Hallucination Leaderboard!", "raw": "🔥 New LLM leaderboard on the hub: an LLM Hallucination Leaderboard!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Led by ", "raw": "Led by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@pminervini", "href": null, "resource": null, "url": null, "code": null, "user": "pminervini", "label": null, "lang": null }, { "type": "text", "value": " , it evaluates the propensity of models to *hallucinate*, either on factuality (= say false things) or faithfulness (= ignore user instructions). This is becoming an increasingly important avenue of research, as more and more people are starting to rely on LLMs to find and search for information! ", "raw": " , it evaluates the propensity of models to *hallucinate*, either on factuality (= say false things) or faithfulness (= ignore user instructions). This is becoming an increasingly important avenue of research, as more and more people are starting to rely on LLMs to find and search for information! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It contains 14 datasets, grouped over 7 concepts, to try to get a better overall view of when LLMs output wrong content.", "raw": "It contains 14 datasets, grouped over 7 concepts, to try to get a better overall view of when LLMs output wrong content.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/hallucinations-leaderboard/leaderboard?logs=container", "href": null, "resource": { "type": "space", "id": "hallucinations-leaderboard/leaderboard", "discussionNum": null }, "url": "https://huggingface.co/spaces/hallucinations-leaderboard/leaderboard?logs=container", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Their introductory blog post also contains an in depth analysis of which LLMs get what wrong, which is super interesting: ", "raw": "Their introductory blog post also contains an in depth analysis of which LLMs get what wrong, which is super interesting: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/leaderboards-on-the-hub-hallucinations", "href": "https://huggingface.co/blog/leaderboards-on-the-hub-hallucinations", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Congrats to the team! 🚀", "raw": "Congrats to the team! 🚀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Current ranking of pre-trained (nonchat) open access LLMs according to the leaderboard. 1-4 are from China-based groups. Does training models with Chinese somehow lead to better metrics? 🤔 WDYT?", "raw": "Current ranking of pre-trained (nonchat) open access LLMs according to the leaderboard. 1-4 are from China-based groups. Does training models with Chinese somehow lead to better metrics? 🤔 WDYT?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. ", "raw": "1. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Qwen/Qwen-72B", "href": null, "resource": { "type": "model", "id": "Qwen/Qwen-72B", "discussionNum": null }, "url": "https://huggingface.co/Qwen/Qwen-72B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. ", "raw": "2. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/01-ai/Yi-34B/tree/main", "href": null, "resource": { "type": "model", "id": "01-ai/Yi-34B", "discussionNum": null }, "url": "https://huggingface.co/01-ai/Yi-34B/tree/main", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3. ", "raw": "3. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/internlm/internlm2-20b", "href": null, "resource": { "type": "model", "id": "internlm/internlm2-20b", "discussionNum": null }, "url": "https://huggingface.co/internlm/internlm2-20b", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "4. ", "raw": "4. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/deepseek-ai/deepseek-llm-67b-base", "href": null, "resource": { "type": "model", "id": "deepseek-ai/deepseek-llm-67b-base", "discussionNum": null }, "url": "https://huggingface.co/deepseek-ai/deepseek-llm-67b-base", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "5. ", "raw": "5. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/mistralai/Mixtral-8x7B-v0.1", "href": null, "resource": { "type": "model", "id": "mistralai/Mixtral-8x7B-v0.1", "discussionNum": null }, "url": "https://huggingface.co/mistralai/Mixtral-8x7B-v0.1", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🔎 DPO hyperparameter search update!", "raw": "🔎 DPO hyperparameter search update!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In my previous post (", "raw": "In my previous post (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/posts/BramVanroy/633544255876795", "href": "https://huggingface.co/posts/BramVanroy/633544255876795", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "), I indicated how despite high reward accuracies and low losses, my model would sometimes just output repeating random tokens (", "raw": "), I indicated how despite high reward accuracies and low losses, my model would sometimes just output repeating random tokens (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`/*****/`", "href": null, "resource": null, "url": null, "code": "/*****/", "user": null, "label": null, "lang": null }, { "type": "text", "value": "). There were some useful brainstorms in that thread. I think the dataset is relatively easy for the model, leading it to quickly overfit when the beta is very small, which allows the model to step away further from its initially outputs.", "raw": "). There were some useful brainstorms in that thread. I think the dataset is relatively easy for the model, leading it to quickly overfit when the beta is very small, which allows the model to step away further from its initially outputs.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "So, I ran a hyperparameter search for learning rate (1e-7 v 5e-7), batch size (32, 64, 96, 128) and most importantly, beta (0.01, 0.1, 0.2, 0.5). You can have a look at the results for yourself here: ", "raw": "So, I ran a hyperparameter search for learning rate (1e-7 v 5e-7), batch size (32, 64, 96, 128) and most importantly, beta (0.01, 0.1, 0.2, 0.5). You can have a look at the results for yourself here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://wandb.ai/bramvanroy/dpo-geitje-ultra-hyperparams", "href": "https://wandb.ai/bramvanroy/dpo-geitje-ultra-hyperparams", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Interpreting the result, I'd think that the beta=0.5 is the better choice for this dataset. Reasons:", "raw": "Interpreting the result, I'd think that the beta=0.5 is the better choice for this dataset. Reasons:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- markedly higher rewards margins compared to all other betas", "raw": "- markedly higher rewards margins compared to all other betas", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- better balance between positive chosen and negative rejected rewards", "raw": "- better balance between positive chosen and negative rejected rewards", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- log probabilities are not as superbly low as for beta=0.01, which seems too low for this dataset", "raw": "- log probabilities are not as superbly low as for beta=0.01, which seems too low for this dataset", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Of course, that is just purely looking at numbers without running any benchmarks. However, I am hesitant to evaluate all the models on benchmarks and, therefore, literally optimising my hyperparameters on a test set (which is very bad!). So I will just play with some of the most promising models and see which one feels \"best\" qualitatively.", "raw": "Of course, that is just purely looking at numbers without running any benchmarks. However, I am hesitant to evaluate all the models on benchmarks and, therefore, literally optimising my hyperparameters on a test set (which is very bad!). So I will just play with some of the most promising models and see which one feels \"best\" qualitatively.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If you have other insights, thoughts, or opinions, let me know!", "raw": "If you have other insights, thoughts, or opinions, let me know!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🔍 Today's pick in Interpretability & Analysis of LMs: LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools by ", "raw": "🔍 Today's pick in Interpretability & Analysis of LMs: LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@qiaw99", "href": null, "resource": null, "url": null, "code": null, "user": "qiaw99", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@tanikina", "href": null, "resource": null, "url": null, "code": null, "user": "tanikina", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@nfel", "href": null, "resource": null, "url": null, "code": null, "user": "nfel", "label": null, "lang": null }, { "type": "text", "value": " et al.", "raw": " et al.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Authors introduce LLMCheckup, a conversational interface connecting an LLM to several interpretability tools (feature attribution methods, similarity, counterfactual/rationale generation) allowing users to inquire about LLM predictions using natural language. The interface consolidates several interpretability methods in a unified chat interface, simplifying future investigations into natural language explanations.", "raw": "Authors introduce LLMCheckup, a conversational interface connecting an LLM to several interpretability tools (feature attribution methods, similarity, counterfactual/rationale generation) allowing users to inquire about LLM predictions using natural language. The interface consolidates several interpretability methods in a unified chat interface, simplifying future investigations into natural language explanations.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Paper: ", "raw": "📄 Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.12576", "href": null, "resource": { "type": "paper", "id": "2401.12576", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.12576", "code": null, "user": null, "label": "LLMCheckup: Conversational Examination of Large Language Models via\n Interpretability Tools (2401.12576)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💻 Code: ", "raw": "💻 Code: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/DFKI-NLP/LLMCheckup", "href": "https://github.com/DFKI-NLP/LLMCheckup", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🎥 Demo video: ", "raw": "🎥 Demo video: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.youtube.com/watch?v=ZwN8ZQSXoOU", "href": "https://www.youtube.com/watch?v=ZwN8ZQSXoOU", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Presenting: SimpleMath", "raw": "Presenting: SimpleMath", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Recently we uploaded on the hub our LATEST and most powerful version of SimpleMath SFT dataset.", "raw": "Recently we uploaded on the hub our LATEST and most powerful version of SimpleMath SFT dataset.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Today we are happy to present SimpleMath DPO Pairs, improving further mathematical capabilities on LLM's.", "raw": "Today we are happy to present SimpleMath DPO Pairs, improving further mathematical capabilities on LLM's.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Our first results shows clear improvements on GSM8k, MATHQA, ARC, TQA, MMLU and BBH. Feel free to experiment and generate your own dataset, as we also provide the code to generate them synthetically. ", "raw": "Our first results shows clear improvements on GSM8k, MATHQA, ARC, TQA, MMLU and BBH. Feel free to experiment and generate your own dataset, as we also provide the code to generate them synthetically. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/fblgit/simple-math", "href": null, "resource": { "type": "dataset", "id": "fblgit/simple-math", "discussionNum": null }, "url": "https://huggingface.co/datasets/fblgit/simple-math", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/fblgit/simple-math-DPO", "href": null, "resource": { "type": "dataset", "id": "fblgit/simple-math-DPO", "discussionNum": null }, "url": "https://huggingface.co/datasets/fblgit/simple-math-DPO", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/fblgit/UNA-34BeagleSimpleMath-32K-v1", "href": null, "resource": { "type": "model", "id": "fblgit/UNA-34BeagleSimpleMath-32K-v1", "discussionNum": null }, "url": "https://huggingface.co/fblgit/UNA-34BeagleSimpleMath-32K-v1", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "A 1b dense causal language model begins to \"saturate\" in terms of accuracy around 5 epochs on 1.2T tokens.", "raw": "A 1b dense causal language model begins to \"saturate\" in terms of accuracy around 5 epochs on 1.2T tokens.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Are you working with users and would like to let them edit a text containing highlights? Check out my new Gradio component ", "raw": "Are you working with users and would like to let them edit a text containing highlights? Check out my new Gradio component ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`HighlightedTextbox`", "href": null, "resource": null, "url": null, "code": "HighlightedTextbox", "user": null, "label": null, "lang": null }, { "type": "text", "value": "! 🤗", "raw": "! 🤗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The component can be installed with ", "raw": "The component can be installed with ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`pip install gradio_highlightedtextbox`", "href": null, "resource": null, "url": null, "code": "pip install gradio_highlightedtextbox", "user": null, "label": null, "lang": null }, { "type": "text", "value": " and used seamlessly with other native components in Gradio interfaces. It supports texts with multiple tags and provides some reasonable UX behaviors (tags disappear when an edit is performed inside them). It should be great for UIs to study user editing of annotated LM generations/translations!", "raw": " and used seamlessly with other native components in Gradio interfaces. It supports texts with multiple tags and provides some reasonable UX behaviors (tags disappear when an edit is performed inside them). It should be great for UIs to study user editing of annotated LM generations/translations!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Demo: ", "raw": "Demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/gsarti/gradio_highlightedtextbox", "href": null, "resource": { "type": "space", "id": "gsarti/gradio_highlightedtextbox", "discussionNum": null }, "url": "https://huggingface.co/spaces/gsarti/gradio_highlightedtextbox", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "👋hi there folks !", "raw": "👋hi there folks !", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "check out this Chest X-Ray model from AIMIStanford : ", "raw": "check out this Chest X-Ray model from AIMIStanford : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Tonic/CheXRay", "href": null, "resource": { "type": "space", "id": "Tonic/CheXRay", "discussionNum": null }, "url": "https://huggingface.co/spaces/Tonic/CheXRay", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "thanks to ", "raw": "thanks to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@lunarflu", "href": null, "resource": null, "url": null, "code": null, "user": "lunarflu", "label": null, "lang": null }, { "type": "text", "value": " for kicking me a bit to get the examples in there ! ", "raw": " for kicking me a bit to get the examples in there ! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "would be great to get even more examples and even more downstream functions , so contributions are very welcome, or if you have a dataset source, please do share it in the discussions !", "raw": "would be great to get even more examples and even more downstream functions , so contributions are very welcome, or if you have a dataset source, please do share it in the discussions !", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Thrilled to share some of our recent work in the field of Multimodal Large Language Models (MLLMs). ", "raw": "Thrilled to share some of our recent work in the field of Multimodal Large Language Models (MLLMs). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1️⃣ A Survey on Multimodal Reasoning 📚", "raw": "1️⃣ A Survey on Multimodal Reasoning 📚", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Are you curious about the reasoning abilities of MLLMs? In our latest survey, we delve into the world of multimodal reasoning. We comprehensively review existing evaluation protocols, categorize the frontiers of MLLMs, explore recent trends in their applications for reasoning-intensive tasks, and discuss current practices and future directions. For an in-depth exploration, check out our paper: ", "raw": "Are you curious about the reasoning abilities of MLLMs? In our latest survey, we delve into the world of multimodal reasoning. We comprehensively review existing evaluation protocols, categorize the frontiers of MLLMs, explore recent trends in their applications for reasoning-intensive tasks, and discuss current practices and future directions. For an in-depth exploration, check out our paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.06805", "href": null, "resource": { "type": "paper", "id": "2401.06805", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.06805", "code": null, "user": null, "label": "Exploring the Reasoning Abilities of Multimodal Large Language Models\n (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning (2401.06805)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2️⃣ Advancing Flamingo with InfiMM 🔥", "raw": "2️⃣ Advancing Flamingo with InfiMM 🔥", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Building upon the foundation of Flamingo, we introduce the InfiMM model series. InfiMM is a reproduction of Flamingo, enhanced with stronger Large Language Models (LLMs) such as LLaMA2-13B, Vicuna-13B, and Zephyr7B. We've meticulously filtered pre-training data and fine-tuned instructions, resulting in superior performance on recent benchmarks like MMMU, InfiMM-Eval, MM-Vet, and more. Explore the power of InfiMM on Huggingface: ", "raw": "Building upon the foundation of Flamingo, we introduce the InfiMM model series. InfiMM is a reproduction of Flamingo, enhanced with stronger Large Language Models (LLMs) such as LLaMA2-13B, Vicuna-13B, and Zephyr7B. We've meticulously filtered pre-training data and fine-tuned instructions, resulting in superior performance on recent benchmarks like MMMU, InfiMM-Eval, MM-Vet, and more. Explore the power of InfiMM on Huggingface: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Infi-MM/infimm-zephyr", "href": null, "resource": { "type": "model", "id": "Infi-MM/infimm-zephyr", "discussionNum": null }, "url": "https://huggingface.co/Infi-MM/infimm-zephyr", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3️⃣ Exploring Multimodal Instruction Fine-tuning 🖼️", "raw": "3️⃣ Exploring Multimodal Instruction Fine-tuning 🖼️", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Visual Instruction Fine-tuning (IFT) is crucial for aligning MLLMs' output with user intentions. Our research identified challenges with models trained on the LLaVA-mix-665k dataset, particularly in multi-round dialog settings. To address this, we've created a new IFT dataset with high-quality, diverse instruction annotations and images sourced exclusively from the COCO dataset. Our experiments demonstrate that when fine-tuned with this dataset, MLLMs excel in open-ended evaluation benchmarks for both single-round and multi-round dialog settings. Dive into the details in our paper: ", "raw": "Visual Instruction Fine-tuning (IFT) is crucial for aligning MLLMs' output with user intentions. Our research identified challenges with models trained on the LLaVA-mix-665k dataset, particularly in multi-round dialog settings. To address this, we've created a new IFT dataset with high-quality, diverse instruction annotations and images sourced exclusively from the COCO dataset. Our experiments demonstrate that when fine-tuned with this dataset, MLLMs excel in open-ended evaluation benchmarks for both single-round and multi-round dialog settings. Dive into the details in our paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.08968", "href": null, "resource": { "type": "paper", "id": "2401.08968", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.08968", "code": null, "user": null, "label": "COCO is \"ALL'' You Need for Visual Instruction Fine-tuning (2401.08968)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Stay tuned for more exciting developments. ", "raw": "Stay tuned for more exciting developments. 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[ { "type": "text", "value": "🔥 New LLM leaderboard on the hub: an LLM Safety Leaderboard!", "raw": "🔥 New LLM leaderboard on the hub: an LLM Safety Leaderboard!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It evaluates LLM safety, such as bias and toxicity, PII, and robustness, and is powered by DecodingTrust (outstanding paper at Neurips!) 🚀", "raw": "It evaluates LLM safety, such as bias and toxicity, PII, and robustness, and is powered by DecodingTrust (outstanding paper at Neurips!) 🚀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/AI-Secure/llm-trustworthy-leaderboard", "href": null, "resource": { "type": "space", "id": "AI-Secure/llm-trustworthy-leaderboard", "discussionNum": null }, "url": "https://huggingface.co/spaces/AI-Secure/llm-trustworthy-leaderboard", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It's great to see such initiatives emerge, trying to understand the risks and biases of LLMs, and I'm hoping other tools will follow. It should be interesting for the community of model builders (whether or not they want uncensored models ^^).", "raw": "It's great to see such initiatives emerge, trying to understand the risks and biases of LLMs, and I'm hoping other tools will follow. It should be interesting for the community of model builders (whether or not they want uncensored models ^^).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Detailed intro blog: ", "raw": "Detailed intro blog: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/leaderboards-on-the-hub-decodingtrust", "href": "https://huggingface.co/blog/leaderboards-on-the-hub-decodingtrust", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ". ", "raw": ". ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Congrats to the AI Secure team!", "raw": "Congrats to the AI Secure team!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Gradio 4.16 introduces a new flow: you can hide/show Tabs or make them interactive/non-interactive. ", "raw": "Gradio 4.16 introduces a new flow: you can hide/show Tabs or make them interactive/non-interactive. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Really nice for multi-step machine learning ademos ⚡️", "raw": "Really nice for multi-step machine learning ademos ⚡️", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Hello everyone,", "raw": "Hello everyone,", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is my first post! I have also decided to release a dataset that I have been keeping private for a while now. I’ve kept it private because I’m not sure if it is actually good or not. I would greatly appreciate it if someone could fine-tune some larger models and evaluate the dataset. Named Hercules-v1.0, it is a turbo-charged version of teknium’s openhermes generated by augmenting its data sources. Learn more in the dataset card: ", "raw": "This is my first post! I have also decided to release a dataset that I have been keeping private for a while now. I’ve kept it private because I’m not sure if it is actually good or not. I would greatly appreciate it if someone could fine-tune some larger models and evaluate the dataset. Named Hercules-v1.0, it is a turbo-charged version of teknium’s openhermes generated by augmenting its data sources. Learn more in the dataset card: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/Locutusque/hercules-v1.0", "href": null, "resource": { "type": "dataset", "id": "Locutusque/hercules-v1.0", "discussionNum": null }, "url": "https://huggingface.co/datasets/Locutusque/hercules-v1.0", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "In 2024, we're expanding from open weights to open EVERYTHING (datasets, training scripts,...). ", "raw": "In 2024, we're expanding from open weights to open EVERYTHING (datasets, training scripts,...). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Excited to see this dataset release in French by ", "raw": "Excited to see this dataset release in French by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Pclanglais", "href": null, "resource": null, "url": null, "code": null, "user": "Pclanglais", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@carbonbasedLLM", "href": null, "resource": null, "url": null, "code": null, "user": "carbonbasedLLM", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@anastasiastasenko", "href": null, "resource": null, "url": null, "code": null, "user": "anastasiastasenko", "label": null, "lang": null }, { "type": "text", "value": ":", "raw": ":", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/PleIAs/French-PD-Newspapers", "href": null, "resource": { "type": "dataset", "id": "PleIAs/French-PD-Newspapers", "discussionNum": null }, "url": "https://huggingface.co/datasets/PleIAs/French-PD-Newspapers", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "\"To give you an idea of the size, the full French Wikipedia is about 2 billon words. This is 40 times larger.\"", "raw": "\"To give you an idea of the size, the full French Wikipedia is about 2 billon words. This is 40 times larger.\"", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Diffuse to Choose", "raw": "Diffuse to Choose", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All", "raw": "Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "paper page: ", "raw": "paper page: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.13795", "href": null, "resource": { "type": "paper", "id": "2401.13795", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.13795", "code": null, "user": null, "label": "Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent\n Diffusion Models for Virtual Try-All (2401.13795)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Diffuse to Choose (DTC) allows users to virtually place any e-commerce item in any setting, ensuring detailed, semantically coherent blending with realistic lighting and shadows.", "raw": "Diffuse to Choose (DTC) allows users to virtually place any e-commerce item in any setting, ensuring detailed, semantically coherent blending with realistic lighting and shadows.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🔍 Today's pick in Interpretability & Analysis of LMs: The Calibration Gap between Model and Human Confidence in Large Language Models by ", "raw": "🔍 Today's pick in Interpretability & Analysis of LMs: The Calibration Gap between Model and Human Confidence in Large Language Models by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@coolprof", "href": null, "resource": null, "url": null, "code": null, "user": "coolprof", "label": null, "lang": null }, { "type": "text", "value": " H. Tejeda A. Kumar ", "raw": " H. Tejeda A. Kumar ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Cbelem", "href": null, "resource": null, "url": null, "code": null, "user": "Cbelem", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@skarny", "href": null, "resource": null, "url": null, "code": null, "user": "skarny", "label": null, "lang": null }, { "type": "text", "value": " et al.", "raw": " et al.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This work involves an experimental study to assess human confidence in LLM responses to multiple-choice MMLU questions based on explanations the LLM provides together with the selected answer. The authors experiment with altering the model prompt to reflect the actual prediction confidence in models’ explanations, showing improved calibration for users’ assessment of LLM’s reliability and a better ability to discriminate between correct and incorrect answers. These results suggest the importance of further research on the impact of automatic explanations on users’ perception.", "raw": "This work involves an experimental study to assess human confidence in LLM responses to multiple-choice MMLU questions based on explanations the LLM provides together with the selected answer. The authors experiment with altering the model prompt to reflect the actual prediction confidence in models’ explanations, showing improved calibration for users’ assessment of LLM’s reliability and a better ability to discriminate between correct and incorrect answers. These results suggest the importance of further research on the impact of automatic explanations on users’ perception.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Paper: ", "raw": "📄 Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.13835", "href": null, "resource": { "type": "paper", "id": "2401.13835", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.13835", "code": null, "user": null, "label": "The Calibration Gap between Model and Human Confidence in Large Language\n Models (2401.13835)", "lang": null } ]

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[ { "type": "text", "value": "If you have to choose one small base language model <=3B for ChatML Code Assistant (SFT+DPO) to validate the approach on the dataset and tune hyperparams, so later retrain with a larger base model like Mistral/Mixtral, what model would you pick?", "raw": "If you have to choose one small base language model <=3B for ChatML Code Assistant (SFT+DPO) to validate the approach on the dataset and tune hyperparams, so later retrain with a larger base model like Mistral/Mixtral, what model would you pick?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🧵", "raw": "🧵", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Hi everyone,", "raw": "Hi everyone,", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For my first post, I'm announcing a big release (in multiple ways): probably the largest open corpus in French to date, with 85 billion words in the public domain.", "raw": "For my first post, I'm announcing a big release (in multiple ways): probably the largest open corpus in French to date, with 85 billion words in the public domain.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The dataset has been prepared in collaboration with Benoît de Courson and Benjamin Azoulay from Gallicagram (", "raw": "The dataset has been prepared in collaboration with Benoît de Courson and Benjamin Azoulay from Gallicagram (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://shiny.ens-paris-saclay.fr/app/gallicagram", "href": "https://shiny.ens-paris-saclay.fr/app/gallicagram", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "). Gallicagram is a major cultural analytics project in French, the open and better version of ngram viewer for large scale search of word and ngram occurrences.", "raw": "). Gallicagram is a major cultural analytics project in French, the open and better version of ngram viewer for large scale search of word and ngram occurrences.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The corpus is made of two different dataset for monographs (16B words) ", "raw": "The corpus is made of two different dataset for monographs (16B words) ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/PleIAs/French-PD-Newspapers", "href": null, "resource": { "type": "dataset", "id": "PleIAs/French-PD-Newspapers", "discussionNum": null }, "url": "https://huggingface.co/datasets/PleIAs/French-PD-Newspapers", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " and newspapers/periodicals (69B) ", "raw": " and newspapers/periodicals (69B) ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/PleIAs/French-PD-Newspapers", "href": null, "resource": { "type": "dataset", "id": "PleIAs/French-PD-Newspapers", "discussionNum": null }, "url": "https://huggingface.co/datasets/PleIAs/French-PD-Newspapers", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " Along with the full text it also includes core provenance metadata.", "raw": " Along with the full text it also includes core provenance metadata.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Beyond research in digital humanities, the corpus can also be used to training open and reproducible LLMs. Being in the public domain means it can be released everywhere in any shape without restrictions.", "raw": "Beyond research in digital humanities, the corpus can also be used to training open and reproducible LLMs. Being in the public domain means it can be released everywhere in any shape without restrictions.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The corpus is not perfect: digitization of cultural heritage is challenging and, especially for newspapers, we tackle with layout issues and a significant rate of optical character recognition mistake. Our conviction is that releasing corpus as a commons is the best way to improve on this. Sharing is caring.", "raw": "The corpus is not perfect: digitization of cultural heritage is challenging and, especially for newspapers, we tackle with layout issues and a significant rate of optical character recognition mistake. Our conviction is that releasing corpus as a commons is the best way to improve on this. Sharing is caring.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "My TED talk is finally live!! I proposed the recipe for the \"Foundation Agent\": a single model that learns how to act in different worlds. LLM scales across lots and lots of texts. Foundation Agent scales across lots of lots of realities. If it is able to master 10,000 diverse simulated realities, it may well generalize to our physical world, which is simply the 10,001st reality.", "raw": "My TED talk is finally live!! I proposed the recipe for the \"Foundation Agent\": a single model that learns how to act in different worlds. LLM scales across lots and lots of texts. Foundation Agent scales across lots of lots of realities. If it is able to master 10,000 diverse simulated realities, it may well generalize to our physical world, which is simply the 10,001st reality.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Why do we want a single Foundation Agent instead of many smaller models? I'd like to quote the idea from my friend Prof. Yuke Zhu's CoRL keynote talk. If we trace each AI field's evolution, we'd find this pattern:", "raw": "Why do we want a single Foundation Agent instead of many smaller models? I'd like to quote the idea from my friend Prof. Yuke Zhu's CoRL keynote talk. If we trace each AI field's evolution, we'd find this pattern:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Specialist -> Generalist -> Specializing Generalist", "raw": "Specialist -> Generalist -> Specializing Generalist", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And the \"specialized generalist\" is often way more powerful than the original specialist. Just like distilled versions of LlaMA are way better than custom-built NLP systems 5 years ago.", "raw": "And the \"specialized generalist\" is often way more powerful than the original specialist. Just like distilled versions of LlaMA are way better than custom-built NLP systems 5 years ago.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "TED talks do not have teleprompters!! All I have is a \"confidence monitor\" at my foot, showing only the current slide and timer. That means I need to memorize the whole speech. It sounds intimidating at first, but turns out to be the best way to connect with the audience and deliver the ideas right to heart. ", "raw": "TED talks do not have teleprompters!! All I have is a \"confidence monitor\" at my foot, showing only the current slide and timer. That means I need to memorize the whole speech. It sounds intimidating at first, but turns out to be the best way to connect with the audience and deliver the ideas right to heart. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Happy to share my slides with all of you! ", "raw": "Happy to share my slides with all of you! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://drive.google.com/file/d/1NSY6MxMu3OPQ4U6hx0OxPq7EQB5XTcAG/view?usp=sharing", "href": "https://drive.google.com/file/d/1NSY6MxMu3OPQ4U6hx0OxPq7EQB5XTcAG/view?usp=sharing", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The video is only 10 min. I promise it's well worth your time! ", "raw": "The video is only 10 min. I promise it's well worth your time! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.ted.com/talks/jim_fan_the_next_grand_challenge_for_ai", "href": "https://www.ted.com/talks/jim_fan_the_next_grand_challenge_for_ai", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Do you have a hidden massive storage leak thanks to HF hub models and datasets revisions adding up and not getting automatically deleted?", "raw": "Do you have a hidden massive storage leak thanks to HF hub models and datasets revisions adding up and not getting automatically deleted?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here is how to delete all old revisions and only keeping ", "raw": "Here is how to delete all old revisions and only keeping ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`main`", "href": null, "resource": null, "url": null, "code": "main", "user": null, "label": null, "lang": null }, { "type": "text", "value": " in a few quick steps and no tedious manual editing.", "raw": " in a few quick steps and no tedious manual editing.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In terminal A:", "raw": "In terminal A:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\n$ pip install huggingface_hub[\"cli\"] -U\n$ huggingface-cli delete-cache --disable-tui\nFile to edit: /tmp/tmpundr7lky.txt\n0 revisions selected counting for 0.0. Continue ? (y/N)\n```", "href": null, "resource": null, "url": null, "code": "$ pip install huggingface_hub[\"cli\"] -U\n$ huggingface-cli delete-cache --disable-tui\nFile to edit: /tmp/tmpundr7lky.txt\n0 revisions selected counting for 0.0. Continue ? (y/N)", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Do not answer the prompt and proceed with my instructions.", "raw": "Do not answer the prompt and proceed with my instructions.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "(note your tmp file will have a different path, so adjust it below)", "raw": "(note your tmp file will have a different path, so adjust it below)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In terminal B:", "raw": "In terminal B:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\n$ cp /tmp/tmpedbz00ox.txt cache.txt\n$ perl -pi -e 's|^#(.*detached.*)|$1|' cache.txt\n$ cat cache.txt >> /tmp/tmpundr7lky.txt\n```", "href": null, "resource": null, "url": null, "code": "$ cp /tmp/tmpedbz00ox.txt cache.txt\n$ perl -pi -e 's|^#(.*detached.*)|$1|' cache.txt\n$ cat cache.txt >> /tmp/tmpundr7lky.txt", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The perl one-liner uncommented out all lines that had ", "raw": "The perl one-liner uncommented out all lines that had ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`(detached)`", "href": null, "resource": null, "url": null, "code": "(detached)", "user": null, "label": null, "lang": null }, { "type": "text", "value": " in it - so can be wiped out. And then we pasted it back into the tmp file ", "raw": " in it - so can be wiped out. And then we pasted it back into the tmp file ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`huggingface-cli`", "href": null, "resource": null, "url": null, "code": "huggingface-cli", "user": null, "label": null, "lang": null }, { "type": "text", "value": " expects to be edited.", "raw": " expects to be edited.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Now go back to terminal A and hit: N, Y, Y, so it looks like:", "raw": "Now go back to terminal A and hit: N, Y, Y, so it looks like:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\n0 revisions selected counting for 0.0. Continue ? (y/N) n\n89 revisions selected counting for 211.7G. Continue ? (y/N) y\n89 revisions selected counting for 211.7G. Confirm deletion ? (Y/n) y\n```", "href": null, "resource": null, "url": null, "code": "0 revisions selected counting for 0.0. Continue ? (y/N) n\n89 revisions selected counting for 211.7G. Continue ? (y/N) y\n89 revisions selected counting for 211.7G. Confirm deletion ? (Y/n) y", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Done.", "raw": "Done.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If you messed up with the prompt answering you still have ", "raw": "If you messed up with the prompt answering you still have ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`cache.txt`", "href": null, "resource": null, "url": null, "code": "cache.txt", "user": null, "label": null, "lang": null }, { "type": "text", "value": " file which you can feed again to the new tmp file it'll create when you run ", "raw": " file which you can feed again to the new tmp file it'll create when you run ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`huggingface-cli delete-cache --disable-tui`", "href": null, "resource": null, "url": null, "code": "huggingface-cli delete-cache --disable-tui", "user": null, "label": null, "lang": null }, { "type": "text", "value": " again.", "raw": " again.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For more details and additional techniques please see ", "raw": "For more details and additional techniques please see ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/stas00/ml-engineering/tree/master/storage#huggingface-hub-caches", "href": "https://github.com/stas00/ml-engineering/tree/master/storage#huggingface-hub-caches", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "New in Gradio 4.16.0 - Galleries as Input 🖼️", "raw": "New in Gradio 4.16.0 - Galleries as Input 🖼️", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Now your users can upload multiple images as input to your AI application and view them in a slick gallery! ", "raw": "Now your users can upload multiple images as input to your AI application and view them in a slick gallery! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Attached is a demo of how this new feature can be used in a photomaker-type application: ", "raw": "Attached is a demo of how this new feature can be used in a photomaker-type application: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/TencentARC/PhotoMaker", "href": null, "resource": { "type": "space", "id": "TencentARC/PhotoMaker", "discussionNum": null }, "url": "https://huggingface.co/spaces/TencentARC/PhotoMaker", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Shout out ", "raw": "Shout out ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@abidlabs", "href": null, "resource": null, "url": null, "code": null, "user": "abidlabs", "label": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@akhaliq", "href": null, "resource": null, "url": null, "code": null, "user": "akhaliq", "label": null, "lang": null }, { "type": "text", "value": " who proposed this feature after seeing some of the workarounds gradio developers were using in the wild to upload multiple images. ", "raw": " who proposed this feature after seeing some of the workarounds gradio developers were using in the wild to upload multiple images. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The gradio team works hard to stay up to date with the latest trends in AI! If there's something missing from the library, file an issue on github! ", "raw": "The gradio team works hard to stay up to date with the latest trends in AI! If there's something missing from the library, file an issue on github! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/gradio-app/gradio/issues", "href": "https://github.com/gradio-app/gradio/issues", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "✨ Excited to release ", "raw": "✨ Excited to release ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`gradio`", "href": null, "resource": null, "url": null, "code": "gradio", "user": null, "label": null, "lang": null }, { "type": "text", "value": " 4.16. New features include:", "raw": " 4.16. New features include:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🐻‍❄️ Native support for Polars Dataframe ", "raw": "🐻‍❄️ Native support for Polars Dataframe ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🖼️ Gallery component can be used as an input", "raw": "🖼️ Gallery component can be used as an input", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "⚡ Much faster streaming for low-latency chatbots", "raw": "⚡ Much faster streaming for low-latency chatbots", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Auto generated docs for custom components", "raw": "📄 Auto generated docs for custom components", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "... and much more! This is HUGE release, so check out everything else in our changelog: ", "raw": "... and much more! This is HUGE release, so check out everything else in our changelog: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/gradio-app/gradio/blob/main/CHANGELOG.md", "href": "https://github.com/gradio-app/gradio/blob/main/CHANGELOG.md", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "We just released the first Text2SQL model for DuckDB 🦆🧠", "raw": "We just released the first Text2SQL model for DuckDB 🦆🧠", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can try it out directly here :", "raw": "You can try it out directly here :", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/motherduckdb/DuckDB-NSQL-7B", "href": null, "resource": { "type": "space", "id": "motherduckdb/DuckDB-NSQL-7B", "discussionNum": null }, "url": "https://huggingface.co/spaces/motherduckdb/DuckDB-NSQL-7B", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "hey there folks , work in progress, but basically celebrating the release of ", "raw": "hey there folks , work in progress, but basically celebrating the release of ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`whisperspeech`", "href": null, "resource": null, "url": null, "code": "whisperspeech", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "just : ", "raw": "just : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```bash\npip install whisperspeech\n```", "href": null, "resource": null, "url": null, "code": "pip install whisperspeech", "user": null, "label": null, "lang": "bash" }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "to get started and check out my demo to do multilingual text to speech including making voice prints using ", "raw": "to get started and check out my demo to do multilingual text to speech including making voice prints using ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`whisperspeech`", "href": null, "resource": null, "url": null, "code": "whisperspeech", "user": null, "label": null, "lang": null }, { "type": "text", "value": "reverse engineering of whisper here : ", "raw": "reverse engineering of whisper here : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Tonic/whisperspeech", "href": null, "resource": { "type": "space", "id": "Tonic/whisperspeech", "discussionNum": null }, "url": "https://huggingface.co/spaces/Tonic/whisperspeech", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "and the model card here : ", "raw": "and the model card here : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collabora/whisperspeech", "href": "https://huggingface.co/collabora/whisperspeech", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "i met collabora on LAION check out LAION here :", "raw": "i met collabora on LAION check out LAION here :", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/laion", "href": "https://huggingface.co/laion", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "I've just published the 23rd edition of the satellite-image-deep-learning newsletter to 8,188 subscribers ", "raw": "I've just published the 23rd edition of the satellite-image-deep-learning newsletter to 8,188 subscribers ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This edition: METEOR, Seeing the roads through the trees 🌴, A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection & Globe230k dataset", "raw": "This edition: METEOR, Seeing the roads through the trees 🌴, A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection & Globe230k dataset", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.satellite-image-deep-learning.com/p/new-discoveries-23", "href": "https://www.satellite-image-deep-learning.com/p/new-discoveries-23", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild", "raw": "Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "paper page: ", "raw": "paper page: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.13627", "href": null, "resource": { "type": "paper", "id": "2401.13627", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.13627", "code": null, "user": null, "label": "Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic\n Image Restoration In the Wild (2401.13627)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. Leveraging multi-modal techniques and advanced generative prior, SUPIR marks a significant advance in intelligent and realistic image restoration. As a pivotal catalyst within SUPIR, model scaling dramatically enhances its capabilities and demonstrates new potential for image restoration. We collect a dataset comprising 20 million high-resolution, high-quality images for model training, each enriched with descriptive text annotations. SUPIR provides the capability to restore images guided by textual prompts, broadening its application scope and potential. Moreover, we introduce negative-quality prompts to further improve perceptual quality. We also develop a restoration-guided sampling method to suppress the fidelity issue encountered in generative-based restoration. Experiments demonstrate SUPIR's exceptional restoration effects and its novel capacity to manipulate restoration through textual prompts.", "raw": "introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. Leveraging multi-modal techniques and advanced generative prior, SUPIR marks a significant advance in intelligent and realistic image restoration. As a pivotal catalyst within SUPIR, model scaling dramatically enhances its capabilities and demonstrates new potential for image restoration. We collect a dataset comprising 20 million high-resolution, high-quality images for model training, each enriched with descriptive text annotations. SUPIR provides the capability to restore images guided by textual prompts, broadening its application scope and potential. Moreover, we introduce negative-quality prompts to further improve perceptual quality. We also develop a restoration-guided sampling method to suppress the fidelity issue encountered in generative-based restoration. Experiments demonstrate SUPIR's exceptional restoration effects and its novel capacity to manipulate restoration through textual prompts.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🔍 Today's pick in Interpretability & Analysis of LMs: Model Editing Can Hurt General Abilities of Large Language Models by J.C. Gu et al.", "raw": "🔍 Today's pick in Interpretability & Analysis of LMs: Model Editing Can Hurt General Abilities of Large Language Models by J.C. Gu et al.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This work raises concerns that gains in factual knowledge after model editing can result in a significant degradation of the general abilities of LLMs. The authors evaluate 4 popular editing methods on 2 LLMs across eight representative tasks, showing model editing does substantially hurt model general abilities. A suggestion is made to prioritize improvements in LLMs' robustness, developing more precise editing methods, and better evaluation benchmarks.", "raw": "This work raises concerns that gains in factual knowledge after model editing can result in a significant degradation of the general abilities of LLMs. The authors evaluate 4 popular editing methods on 2 LLMs across eight representative tasks, showing model editing does substantially hurt model general abilities. A suggestion is made to prioritize improvements in LLMs' robustness, developing more precise editing methods, and better evaluation benchmarks.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Paper: ", "raw": "📄 Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.04700", "href": null, "resource": { "type": "paper", "id": "2401.04700", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.04700", "code": null, "user": null, "label": "Model Editing Can Hurt General Abilities of Large Language Models (2401.04700)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💻 Code: ", "raw": "💻 Code: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/JasonForJoy/Model-Editing-Hurt", "href": "https://github.com/JasonForJoy/Model-Editing-Hurt", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Can you merge models of different sizes? ⚗️", "raw": "Can you merge models of different sizes? ⚗️", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Well, yes, if the models are somewhat compatible. Here is an experiment I did. I wanted to merge two of the best performing models: ", "raw": "Well, yes, if the models are somewhat compatible. Here is an experiment I did. I wanted to merge two of the best performing models: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/mlabonne/NeuralBeagle14-7B", "href": null, "resource": { "type": "model", "id": "mlabonne/NeuralBeagle14-7B", "discussionNum": null }, "url": "https://huggingface.co/mlabonne/NeuralBeagle14-7B", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v4", "href": null, "resource": { "type": "model", "id": "jeonsworld/CarbonVillain-en-10.7B-v4", "discussionNum": null }, "url": "https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v4", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here is my recipe:", "raw": "Here is my recipe:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. Expand the layers of NeuralBeagle to 10.7B ala frankenmerge.", "raw": "1. Expand the layers of NeuralBeagle to 10.7B ala frankenmerge.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. DPO-tune the previous model with a high-quality preference dataset, ", "raw": "2. DPO-tune the previous model with a high-quality preference dataset, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs", "href": null, "resource": { "type": "dataset", "id": "argilla/distilabel-intel-orca-dpo-pairs", "discussionNum": null }, "url": "https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3. Merge the previous model with CarbonVillain (needs —allow-crimes in mergekit! 🔪)", "raw": "3. Merge the previous model with CarbonVillain (needs —allow-crimes in mergekit! 🔪)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And here is the resulting model, CarbonBeagle-11B, which ranked top in the leaderboard for its size class:", "raw": "And here is the resulting model, CarbonBeagle-11B, which ranked top in the leaderboard for its size class:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/vicgalle/CarbonBeagle-11B", "href": null, "resource": { "type": "model", "id": "vicgalle/CarbonBeagle-11B", "discussionNum": null }, "url": "https://huggingface.co/vicgalle/CarbonBeagle-11B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "LUMIERE", "raw": "LUMIERE", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "A Space-Time Diffusion Model for Video Generation", "raw": "A Space-Time Diffusion Model for Video Generation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "paper page: ", "raw": "paper page: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.12945", "href": null, "resource": { "type": "paper", "id": "2401.12945", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.12945", "code": null, "user": null, "label": "Lumiere: A Space-Time Diffusion Model for Video Generation (2401.12945)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Demonstrate state-of-the-art text-to-video generation results, and show that our design easily facilitates a wide range of content creation tasks and video editing applications, including image-to-video, video inpainting, and stylized generation.", "raw": "Demonstrate state-of-the-art text-to-video generation results, and show that our design easily facilitates a wide range of content creation tasks and video editing applications, including image-to-video, video inpainting, and stylized generation.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "GPU Poor POV: Willingness of Customization ", "raw": "GPU Poor POV: Willingness of Customization ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I love to use libraries in which you can customize a lot of things. Chromadb is my choice of db if it comes to store embeddings. Te cool feature is that you can define your own embeddings function which can be called on every chromadb collection initialisation or creation. It is useful because sometimes we want to use different prompts, different models, and it can be easily written as inheritence from EmbeddingFunction class.", "raw": "I love to use libraries in which you can customize a lot of things. Chromadb is my choice of db if it comes to store embeddings. Te cool feature is that you can define your own embeddings function which can be called on every chromadb collection initialisation or creation. It is useful because sometimes we want to use different prompts, different models, and it can be easily written as inheritence from EmbeddingFunction class.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Edit: ", "raw": "Edit: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "My CustomEmbeddingFunction can be found here:", "raw": "My CustomEmbeddingFunction can be found here:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://gist.github.com/s3nh/cfbbf43f5e9e3cfe8c3e4e2f0d550b80", "href": "https://gist.github.com/s3nh/cfbbf43f5e9e3cfe8c3e4e2f0d550b80", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "and you can use it by initializing or calling the chroma collection. ", "raw": "and you can use it by initializing or calling the chroma collection. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```python\n\nimport chromadb \nfrom your_custom_fn import CustomEmbeddingFunction\nclass ChromaStorage:\n def __init__(self, config):\n self.config = config\n self.client = self.init_client()\n self.embedding_function = CustomEmbeddingFunction()\n\n def check_config(self):\n assert os.path.exists(self.config.path), ValueError('Provided path does not exists!!')\n\n def init_client(self):\n return chromadb.PersistentClient(path = self.config.path,)\n\n def init_collection(self, name: str): \n return self.client.get_or_create_collection(name = name, embedding_function = self.embedding_function)\n```", "href": null, "resource": null, "url": null, "code": "\nimport chromadb \nfrom your_custom_fn import CustomEmbeddingFunction\nclass ChromaStorage:\n def __init__(self, config):\n self.config = config\n self.client = self.init_client()\n self.embedding_function = CustomEmbeddingFunction()\n\n def check_config(self):\n assert os.path.exists(self.config.path), ValueError('Provided path does not exists!!')\n\n def init_client(self):\n return chromadb.PersistentClient(path = self.config.path,)\n\n def init_collection(self, name: str): \n return self.client.get_or_create_collection(name = name, embedding_function = self.embedding_function)", "user": null, "label": null, "lang": "python" } ]

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[ { "type": "text", "value": "👋 Hi there folks,", "raw": "👋 Hi there folks,", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I launched my first competition ! ", "raw": "I launched my first competition ! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Goal : Use AI to beat the Math Olympics within the set time ", "raw": "Goal : Use AI to beat the Math Olympics within the set time ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Basically we're looking for adventurous teams and individuals to make a common submission to the AI Math Olympics by the MLCommons. ", "raw": "Basically we're looking for adventurous teams and individuals to make a common submission to the AI Math Olympics by the MLCommons. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Althought the ultimately there can only be one winner and there must always be a winner, the ultimate goal is to get together for a common solution.", "raw": "Althought the ultimately there can only be one winner and there must always be a winner, the ultimate goal is to get together for a common solution.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "check it out here : ", "raw": "check it out here : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Tonic1/mathathon", "href": null, "resource": { "type": "space", "id": "Tonic1/mathathon", "discussionNum": null }, "url": "https://huggingface.co/spaces/Tonic1/mathathon", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🏅 New top model on the GAIA benchmark!", "raw": "🏅 New top model on the GAIA benchmark!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Called FRIDAY, it's a mysterious new autonomous agent, which got quite good performances on both the public validation set *and* the private test set. ", "raw": "Called FRIDAY, it's a mysterious new autonomous agent, which got quite good performances on both the public validation set *and* the private test set. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It notably passed 10 points for the val and 5 points for the test set on our hardest questions (level 3): they require to take arbitrarily long sequences of actions, use any number of tools, and access the world in genera! ✨", "raw": "It notably passed 10 points for the val and 5 points for the test set on our hardest questions (level 3): they require to take arbitrarily long sequences of actions, use any number of tools, and access the world in genera! ✨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The GAIA benchmark evaluates next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc) and was co authored by ", "raw": "The GAIA benchmark evaluates next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc) and was co authored by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@gregmialz", "href": null, "resource": null, "url": null, "code": null, "user": "gregmialz", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@ThomasNLG", "href": null, "resource": null, "url": null, "code": null, "user": "ThomasNLG", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@ylecun", "href": null, "resource": null, "url": null, "code": null, "user": "ylecun", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@thomwolf", "href": null, "resource": null, "url": null, "code": null, "user": "thomwolf", "label": null, "lang": null }, { "type": "text", "value": " and myself: ", "raw": " and myself: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/gaia-benchmark/leaderboard", "href": null, "resource": { "type": "space", "id": "gaia-benchmark/leaderboard", "discussionNum": null }, "url": "https://huggingface.co/spaces/gaia-benchmark/leaderboard", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🔍 Today's pick in Interpretability & Analysis of LMs: From Understanding to Utilization: A Survey on Explainability for Large Language Models by H. Luo and L. Specia", "raw": "🔍 Today's pick in Interpretability & Analysis of LMs: From Understanding to Utilization: A Survey on Explainability for Large Language Models by H. Luo and L. Specia", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This survey summarizes recent works in interpretability research, focusing mainly on pre-trained Transformer-based LMs. The authors categorize current approaches as either local or global and discuss popular applications of LM interpretability, such as model editing, enhancing model performance, and controlling LM generation.", "raw": "This survey summarizes recent works in interpretability research, focusing mainly on pre-trained Transformer-based LMs. The authors categorize current approaches as either local or global and discuss popular applications of LM interpretability, such as model editing, enhancing model performance, and controlling LM generation.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Paper: ", "raw": "📄 Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.12874", "href": null, "resource": { "type": "paper", "id": "2401.12874", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.12874", "code": null, "user": null, "label": "From Understanding to Utilization: A Survey on Explainability for Large\n Language Models (2401.12874)", "lang": null } ]

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[ { "type": "text", "value": "Making gradio's auto-reload leaner and more robust 💪", "raw": "Making gradio's auto-reload leaner and more robust 💪", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Auto-reload is one of the coolest (and maybe underrated) features of gradio in my opinion. It automatically detects changes in your python app file and hot swaps your backend and frontend without restarting the server. I may be biased, but that's a way nicer experience than most other reload mode features out there.", "raw": "Auto-reload is one of the coolest (and maybe underrated) features of gradio in my opinion. It automatically detects changes in your python app file and hot swaps your backend and frontend without restarting the server. I may be biased, but that's a way nicer experience than most other reload mode features out there.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is all possible due to novel use of python's built in importlib module. Since this is a non-standard use of the module, our users have encountered some rough edges in the wild.", "raw": "This is all possible due to novel use of python's built in importlib module. Since this is a non-standard use of the module, our users have encountered some rough edges in the wild.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I'm happy to announce two improvements that will be out in the next release of gradio. They were both made possible by collaboration in the open source community.", "raw": "I'm happy to announce two improvements that will be out in the next release of gradio. They were both made possible by collaboration in the open source community.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. First, gradio's reload mode now works with the ", "raw": "1. First, gradio's reload mode now works with the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`python-dotenv`", "href": null, "resource": null, "url": null, "code": "python-dotenv", "user": null, "label": null, "lang": null }, { "type": "text", "value": " library. Getting to the bottom of this one took a lot of sleuthing by our users and the fix got merged and released into the ", "raw": " library. Getting to the bottom of this one took a lot of sleuthing by our users and the fix got merged and released into the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`python-dotenv`", "href": null, "resource": null, "url": null, "code": "python-dotenv", "user": null, "label": null, "lang": null }, { "type": "text", "value": " package last night! Thanks to ", "raw": " package last night! Thanks to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@theskumar", "href": null, "resource": null, "url": null, "code": null, "user": "theskumar", "label": null, "lang": null }, { "type": "text", "value": ", maintainer of python-dotenv, for all the help. ", "raw": ", maintainer of python-dotenv, for all the help. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. Second, gradio's reload mode now consumes an order of magnitude less CPU. Depending on how many files are in your source directory, you may see between 2x to 10x less CPU utilization. Thanks to ", "raw": "2. Second, gradio's reload mode now consumes an order of magnitude less CPU. Depending on how many files are in your source directory, you may see between 2x to 10x less CPU utilization. Thanks to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@velaia", "href": null, "resource": null, "url": null, "code": null, "user": "velaia", "label": null, "lang": null }, { "type": "text", "value": " for filing the issue and ", "raw": " for filing the issue and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@abidlabs", "href": null, "resource": null, "url": null, "code": null, "user": "abidlabs", "label": null, "lang": null }, { "type": "text", "value": " for the review!", "raw": " for the review!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "More improvements to reload mode are planned. Stay tuned!", "raw": "More improvements to reload mode are planned. Stay tuned!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🕵️ Looking for DPO experts!", "raw": "🕵️ Looking for DPO experts!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I have a dataset that is gpt-4-turbo as chosen and a lower performing model as rejected. The objective should therefore be fairly easy because the two are easy to discern. As a consequence, the model achieves very low losses (0.021 train; 0.013 validation) and high reward accuracies (0.995). **However**, when using the model in practice, it often detoriates after the first one or two tokens and continuously outputs sequences of ", "raw": "I have a dataset that is gpt-4-turbo as chosen and a lower performing model as rejected. The objective should therefore be fairly easy because the two are easy to discern. As a consequence, the model achieves very low losses (0.021 train; 0.013 validation) and high reward accuracies (0.995). **However**, when using the model in practice, it often detoriates after the first one or two tokens and continuously outputs sequences of ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`/*****/`", "href": null, "resource": null, "url": null, "code": "/*****/", "user": null, "label": null, "lang": null }, { "type": "text", "value": ". So despite the good performance on the DPO objective and strong scores on the validation set (no overfitting), something seems to go wrong. Perhaps the outputs are too different and the task is too easy, in which case DPO is not useful. But why then would the model start hallucinating and repeating the same token over and over again?", "raw": ". So despite the good performance on the DPO objective and strong scores on the validation set (no overfitting), something seems to go wrong. Perhaps the outputs are too different and the task is too easy, in which case DPO is not useful. But why then would the model start hallucinating and repeating the same token over and over again?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Any thoughts? Any suggestions to get around this? All discussions are welcome!", "raw": "Any thoughts? Any suggestions to get around this? All discussions are welcome!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "✅New paper to ensure valid LLM output with SOTA LLMs like GPT4 by mixing it with OSS LLMs", "raw": "✅New paper to ensure valid LLM output with SOTA LLMs like GPT4 by mixing it with OSS LLMs", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper: arxiv.org/abs/2401.09967", "raw": "Paper: arxiv.org/abs/2401.09967", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Great paper showing how strong proprietary AI like #GPT4 can be paired with #OSS LLM to ensure LLM output validity, e.g. valid JSON. ", "raw": "Great paper showing how strong proprietary AI like #GPT4 can be paired with #OSS LLM to ensure LLM output validity, e.g. valid JSON. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Many devs complain that #LLMs cannot be reliably used in production if the output is not valid, for instance, if one wants to use LLMs to generate SQL queries or JSON, it is crucial that the output is valid.", "raw": "Many devs complain that #LLMs cannot be reliably used in production if the output is not valid, for instance, if one wants to use LLMs to generate SQL queries or JSON, it is crucial that the output is valid.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Frameworks have arisen to constrain the outputs of the LLM to follow some constraints, like outlines (", "raw": "Frameworks have arisen to constrain the outputs of the LLM to follow some constraints, like outlines (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/outlines-dev/outlines", "href": "https://github.com/outlines-dev/outlines", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "), but they assume access to logits.", "raw": "), but they assume access to logits.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This makes them incompatible with proprietary LLMs like GPT4 that don’t share logits, so one can only use open-source LLMs that are much less performant.", "raw": "This makes them incompatible with proprietary LLMs like GPT4 that don’t share logits, so one can only use open-source LLMs that are much less performant.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This paper shows how can use powerful proprietary LLMs like GPT4 to create a first unconstrained sketch and refine it using an OSS model like Llama 2 where logits are accessible, to rewrite the sketch following some specific constraints.", "raw": "This paper shows how can use powerful proprietary LLMs like GPT4 to create a first unconstrained sketch and refine it using an OSS model like Llama 2 where logits are accessible, to rewrite the sketch following some specific constraints.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "They show that GPT4 Precision can be increased by 14% (43% before, 57% after), by boosting it with constrained output on information extraction on Wiki-NRE!", "raw": "They show that GPT4 Precision can be increased by 14% (43% before, 57% after), by boosting it with constrained output on information extraction on Wiki-NRE!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "What's the best way to fine-tune open LLMs in 2024? Look no further! 👀 I am excited to share “How to Fine-Tune LLMs in 2024 with Hugging Face” using the latest research techniques, including Flash Attention, Q-LoRA, OpenAI dataset formats (messages), ChatML, Packing, all built with Hugging Face TRL. 🚀", "raw": "What's the best way to fine-tune open LLMs in 2024? Look no further! 👀 I am excited to share “How to Fine-Tune LLMs in 2024 with Hugging Face” using the latest research techniques, including Flash Attention, Q-LoRA, OpenAI dataset formats (messages), ChatML, Packing, all built with Hugging Face TRL. 🚀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It is created for consumer-size GPUs (24GB) covering the full end-to-end lifecycle with: ", "raw": "It is created for consumer-size GPUs (24GB) covering the full end-to-end lifecycle with: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💡Define and understand use cases for fine-tuning", "raw": "💡Define and understand use cases for fine-tuning", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🧑🏻‍💻 Setup of the development environment", "raw": "🧑🏻‍💻 Setup of the development environment", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🧮 Create and prepare dataset (OpenAI format)", "raw": "🧮 Create and prepare dataset (OpenAI format)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🏋️‍♀️ Fine-tune LLM using TRL and the SFTTrainer", "raw": "🏋️‍♀️ Fine-tune LLM using TRL and the SFTTrainer", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🥇 Test and evaluate the LLM", "raw": "🥇 Test and evaluate the LLM", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🚀 Deploy for production with TGI", "raw": "🚀 Deploy for production with TGI", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "👉  ", "raw": "👉  ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.philschmid.de/fine-tune-llms-in-2024-with-trl", "href": "https://www.philschmid.de/fine-tune-llms-in-2024-with-trl", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Coming soon: Advanced Guides for multi-GPU/multi-Node full fine-tuning and alignment using DPO & KTO. 🔜", "raw": "Coming soon: Advanced Guides for multi-GPU/multi-Node full fine-tuning and alignment using DPO & KTO. 🔜", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark", "raw": "CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "paper page: ", "raw": "paper page: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.11944", "href": null, "resource": { "type": "paper", "id": "2401.11944", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.11944", "code": null, "user": null, "label": "CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding\n Benchmark (2401.11944)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "As the capabilities of large multimodal models (LMMs) continue to advance, evaluating the performance of LMMs emerges as an increasing need. Additionally, there is an even larger gap in evaluating the advanced knowledge and reasoning abilities of LMMs in non-English contexts such as Chinese. We introduce CMMMU, a new Chinese Massive Multi-discipline Multimodal Understanding benchmark designed to evaluate LMMs on tasks demanding college-level subject knowledge and deliberate reasoning in a Chinese context. CMMMU is inspired by and strictly follows the annotation and analysis pattern of MMMU. CMMMU includes 12k manually collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering, like its companion, MMMU. These questions span 30 subjects and comprise 39 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures. CMMMU focuses on complex perception and reasoning with domain-specific knowledge in the Chinese context. We evaluate 11 open-source LLMs and one proprietary GPT-4V(ision). Even GPT-4V only achieves accuracies of 42%, indicating a large space for improvement. CMMMU will boost the community to build the next-generation LMMs towards expert artificial intelligence and promote the democratization of LMMs by providing diverse language contexts.", "raw": "As the capabilities of large multimodal models (LMMs) continue to advance, evaluating the performance of LMMs emerges as an increasing need. Additionally, there is an even larger gap in evaluating the advanced knowledge and reasoning abilities of LMMs in non-English contexts such as Chinese. We introduce CMMMU, a new Chinese Massive Multi-discipline Multimodal Understanding benchmark designed to evaluate LMMs on tasks demanding college-level subject knowledge and deliberate reasoning in a Chinese context. CMMMU is inspired by and strictly follows the annotation and analysis pattern of MMMU. CMMMU includes 12k manually collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering, like its companion, MMMU. These questions span 30 subjects and comprise 39 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures. CMMMU focuses on complex perception and reasoning with domain-specific knowledge in the Chinese context. We evaluate 11 open-source LLMs and one proprietary GPT-4V(ision). Even GPT-4V only achieves accuracies of 42%, indicating a large space for improvement. CMMMU will boost the community to build the next-generation LMMs towards expert artificial intelligence and promote the democratization of LMMs by providing diverse language contexts.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🔍 Today's pick in Interpretability & Analysis of LMs: Universal Neurons in GPT2 Language Models by ", "raw": "🔍 Today's pick in Interpretability & Analysis of LMs: Universal Neurons in GPT2 Language Models by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@wesg", "href": null, "resource": null, "url": null, "code": null, "user": "wesg", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@thorsley", "href": null, "resource": null, "url": null, "code": null, "user": "thorsley", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@luminolblue", "href": null, "resource": null, "url": null, "code": null, "user": "luminolblue", "label": null, "lang": null }, { "type": "text", "value": " T.R. Kheirkhah Q. Sun, ", "raw": " T.R. Kheirkhah Q. Sun, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@willhath", "href": null, "resource": null, "url": null, "code": null, "user": "willhath", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@NeelNanda", "href": null, "resource": null, "url": null, "code": null, "user": "NeelNanda", "label": null, "lang": null }, { "type": "text", "value": " D. Bertsimas", "raw": " D. Bertsimas", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This work investigates the universality of individual neurons across GPT2 models trained from different initial random seeds, starting from the assumption that such neurons are likely to exhibit interpretable patterns. The authors find 1-5% of neurons showing high correlation across five model seeds, i.e. consistently activating for the same inputs. In particular, those neurons can be grouped into families exhibiting similar functional roles, e.g. modulating the next token prediction entropy, controlling the output norm of an attention head, and promoting/suppressing vocabulary elements in the prediction. Finally, universal neurons are often observed to form antipodal pairs, conjectured to improve the robustness and calibration of model predictions via ensembling.", "raw": "This work investigates the universality of individual neurons across GPT2 models trained from different initial random seeds, starting from the assumption that such neurons are likely to exhibit interpretable patterns. The authors find 1-5% of neurons showing high correlation across five model seeds, i.e. consistently activating for the same inputs. In particular, those neurons can be grouped into families exhibiting similar functional roles, e.g. modulating the next token prediction entropy, controlling the output norm of an attention head, and promoting/suppressing vocabulary elements in the prediction. Finally, universal neurons are often observed to form antipodal pairs, conjectured to improve the robustness and calibration of model predictions via ensembling.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Paper: ", "raw": "📄 Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.12181", "href": null, "resource": { "type": "paper", "id": "2401.12181", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.12181", "code": null, "user": null, "label": "Universal Neurons in GPT2 Language Models (2401.12181)", "lang": null } ]

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[ { "type": "text", "value": "Hi Community! ", "raw": "Hi Community! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I'm ecstatic to announce Flutter SDK for HuggingFace Inference APIs. The project is open-source and can be found at ", "raw": "I'm ecstatic to announce Flutter SDK for HuggingFace Inference APIs. The project is open-source and can be found at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/shivance/huggingface.dart", "href": "https://github.com/shivance/huggingface.dart", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " . I have also created a sample Flutter App that does language translation using HuggingFace's Helsinki-NLP/opus-mt-ru-en\" model. Below is the video demo. ", "raw": " . I have also created a sample Flutter App that does language translation using HuggingFace's Helsinki-NLP/opus-mt-ru-en\" model. Below is the video demo. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "## Why Flutter?", "raw": "## Why Flutter?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Flutter is an open-source UI software development kit created by Google. It is used to develop cross-platform applications from a single codebase for any Web Browser, Android, iOS, Linux, macOS, and Windows.", "raw": "Flutter is an open-source UI software development kit created by Google. It is used to develop cross-platform applications from a single codebase for any Web Browser, Android, iOS, Linux, macOS, and Windows.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Over the past few years, the framework has grown a lot of popularity. ", "raw": "Over the past few years, the framework has grown a lot of popularity. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "## Mission", "raw": "## Mission", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is a small step-stone to bring Open Source Machine Learning to the edge and thus make AI more accessible. This project, at the time of writing, supports only NLP APIs, I'll slowly add other modalities too.", "raw": "This is a small step-stone to bring Open Source Machine Learning to the edge and thus make AI more accessible. This project, at the time of writing, supports only NLP APIs, I'll slowly add other modalities too.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "𝗛𝗼𝘄 𝘄𝗲 𝗺𝗮𝗱𝗲 𝗚𝗿𝗮𝗱𝗶𝗼 𝗳𝗮𝘀𝘁𝗲𝗿 𝗯𝘆... 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝗶𝘁 𝗱𝗼𝘄𝗻!", "raw": "𝗛𝗼𝘄 𝘄𝗲 𝗺𝗮𝗱𝗲 𝗚𝗿𝗮𝗱𝗶𝗼 𝗳𝗮𝘀𝘁𝗲𝗿 𝗯𝘆... 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝗶𝘁 𝗱𝗼𝘄𝗻!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "About a month ago, ", "raw": "About a month ago, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@oobabooga", "href": null, "resource": null, "url": null, "code": null, "user": "oobabooga", "label": null, "lang": null }, { "type": "text", "value": " (who built the popular text generation webui) reported an interesting issue to the Gradio team. After upgrading to Gradio 4, ", "raw": " (who built the popular text generation webui) reported an interesting issue to the Gradio team. After upgrading to Gradio 4, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@oobabooga", "href": null, "resource": null, "url": null, "code": null, "user": "oobabooga", "label": null, "lang": null }, { "type": "text", "value": " noticed that chatbots that streamed very quickly had a lag before their text would show up in the Gradio app. ", "raw": " noticed that chatbots that streamed very quickly had a lag before their text would show up in the Gradio app. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "After some investigation, we determined that the Gradio frontend would receive the updates from the backend immediately, but the browser would lag before rendering the changes on the screen. The main difference between Gradio 3 and Gradio 4 was that we migrated the communication protocol between the backend and frontend from Websockets (WS) to Server-Side Events (SSE), but we couldn't figure out why this would affect the browser's ability to render the streaming updates it was receiving.", "raw": "After some investigation, we determined that the Gradio frontend would receive the updates from the backend immediately, but the browser would lag before rendering the changes on the screen. The main difference between Gradio 3 and Gradio 4 was that we migrated the communication protocol between the backend and frontend from Websockets (WS) to Server-Side Events (SSE), but we couldn't figure out why this would affect the browser's ability to render the streaming updates it was receiving.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "After diving deep into browsers events, ", "raw": "After diving deep into browsers events, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@aliabid94", "href": null, "resource": null, "url": null, "code": null, "user": "aliabid94", "label": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@pngwn", "href": null, "resource": null, "url": null, "code": null, "user": "pngwn", "label": null, "lang": null }, { "type": "text", "value": " made a realization: most browsers treat WS events (specifically the ", "raw": " made a realization: most browsers treat WS events (specifically the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`WebSocket.onmessage`", "href": null, "resource": null, "url": null, "code": "WebSocket.onmessage", "user": null, "label": null, "lang": null }, { "type": "text", "value": " function) with a lower priority than SSE events (", "raw": " function) with a lower priority than SSE events (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`EventSource.onmessage`", "href": null, "resource": null, "url": null, "code": "EventSource.onmessage", "user": null, "label": null, "lang": null }, { "type": "text", "value": " function), which allowed the browser to repaint the window between WS messages. With SSE, the streaming updates would stack up in the browser's event stack and be prioritized over any browser repaint. The browser would eventually clear the stack but it would take some time to go through each update, which produced a lag.", "raw": " function), which allowed the browser to repaint the window between WS messages. With SSE, the streaming updates would stack up in the browser's event stack and be prioritized over any browser repaint. The browser would eventually clear the stack but it would take some time to go through each update, which produced a lag.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We debated different options, but the solution that we implemented was to introduce throttling: we slowed down how frequently we would push updates to the browser event stack to a maximum rate of 20/sec. Although this seemingly “slowed down” Gradio streaming, it actually would allow browsers to process updates in real-time and provide a much better experience to end users of Gradio apps. ", "raw": "We debated different options, but the solution that we implemented was to introduce throttling: we slowed down how frequently we would push updates to the browser event stack to a maximum rate of 20/sec. Although this seemingly “slowed down” Gradio streaming, it actually would allow browsers to process updates in real-time and provide a much better experience to end users of Gradio apps. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "See the PR here: ", "raw": "See the PR here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/gradio-app/gradio/pull/7084", "href": "https://github.com/gradio-app/gradio/pull/7084", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Kudos to ", "raw": "Kudos to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@aliabid94", "href": null, "resource": null, "url": null, "code": null, "user": "aliabid94", "label": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@pngwn", "href": null, "resource": null, "url": null, "code": null, "user": "pngwn", "label": null, "lang": null }, { "type": "text", "value": " for the fix, and to ", "raw": " for the fix, and to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@oobabooga", "href": null, "resource": null, "url": null, "code": null, "user": "oobabooga", "label": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@pseudotensor", "href": null, "resource": null, "url": null, "code": null, "user": "pseudotensor", "label": null, "lang": null }, { "type": "text", "value": " for helping us test it out!", "raw": " for helping us test it out!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Re-posting ", "raw": "Re-posting ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@karpathy", "href": null, "resource": null, "url": null, "code": null, "user": "karpathy", "label": null, "lang": null }, { "type": "text", "value": "'s blogpost here because it's down on ", "raw": "'s blogpost here because it's down on ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://karpathy.github.io/2024/01/21/selfdriving-agi", "href": "https://karpathy.github.io/2024/01/21/selfdriving-agi", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ". What do you all think?", "raw": ". What do you all think?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data", "raw": "Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "paper page: ", "raw": "paper page: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.10891", "href": null, "resource": { "type": "paper", "id": "2401.10891", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.10891", "code": null, "user": null, "label": "Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data (2401.10891)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "demo: ", "raw": "demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/LiheYoung/Depth-Anything", "href": null, "resource": { "type": "space", "id": "LiheYoung/Depth-Anything", "discussionNum": null }, "url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Depth Anything is trained on 1.5M labeled images and 62M+ unlabeled images jointly, providing the most capable Monocular Depth Estimation (MDE) foundation models with the following features:", "raw": "Depth Anything is trained on 1.5M labeled images and 62M+ unlabeled images jointly, providing the most capable Monocular Depth Estimation (MDE) foundation models with the following features:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "zero-shot relative depth estimation, better than MiDaS v3.1 (BEiTL-512)", "raw": "zero-shot relative depth estimation, better than MiDaS v3.1 (BEiTL-512)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "zero-shot metric depth estimation, better than ZoeDepth", "raw": "zero-shot metric depth estimation, better than ZoeDepth", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "optimal in-domain fine-tuning and evaluation on NYUv2 and KITTI", "raw": "optimal in-domain fine-tuning and evaluation on NYUv2 and KITTI", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "GPU Poor POV: Dont be Afraid :D", "raw": "GPU Poor POV: Dont be Afraid :D", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Sometimes we dont want to do something because of low self esteem,", "raw": "Sometimes we dont want to do something because of low self esteem,", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I ofter hear 'its to hard for me','i am not an expert','i do not know how to do it', etc. These words are never the truth, we should not be afraid and try to build something because there is no additive value without a failure. ", "raw": "I ofter hear 'its to hard for me','i am not an expert','i do not know how to do it', etc. These words are never the truth, we should not be afraid and try to build something because there is no additive value without a failure. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Same things comes in LLMs, there is a lot of fancy words happening, but whats is more important is that there are also people who are constantly building so other can build. Diving into finetuning LLMs is incredibly simple if we assume using axolotl library and pretrains stored on huggingface. ", "raw": "Same things comes in LLMs, there is a lot of fancy words happening, but whats is more important is that there are also people who are constantly building so other can build. Diving into finetuning LLMs is incredibly simple if we assume using axolotl library and pretrains stored on huggingface. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "All we need is an idea, our GPU Poor desktop or colab notebooks and these steps:", "raw": "All we need is an idea, our GPU Poor desktop or colab notebooks and these steps:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\ngit clone https://github.com/OpenAccess-AI-Collective/axolotl\ncd axolotl\n\npip3 install packaging\npip3 install -e '.[flash-attn,deepspeed]'\n```", "href": null, "resource": null, "url": null, "code": "git clone https://github.com/OpenAccess-AI-Collective/axolotl\ncd axolotl\n\npip3 install packaging\npip3 install -e '.[flash-attn,deepspeed]'", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "After installation process we can go to examples, and modify configs to our own needs. ", "raw": "After installation process we can go to examples, and modify configs to our own needs. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Lets jump into ", "raw": "Lets jump into ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\naxolotl\\examples\\llama-2\\qlora.yml \n```", "href": null, "resource": null, "url": null, "code": "axolotl\\examples\\llama-2\\qlora.yml ", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "and change ", "raw": "and change ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\nbase_model: NousResearch/Llama-2-7b-hf\n```", "href": null, "resource": null, "url": null, "code": "base_model: NousResearch/Llama-2-7b-hf", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "to ", "raw": "to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\nbase_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0\n```", "href": null, "resource": null, "url": null, "code": "base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "choose dataset from huge amounts of dataset that are possible to use from hf.co/datasets and tweak additional params like batch_size, number of epochs, how often do we want to save our model and many more (which I wont focus on rn). ", "raw": "choose dataset from huge amounts of dataset that are possible to use from hf.co/datasets and tweak additional params like batch_size, number of epochs, how often do we want to save our model and many more (which I wont focus on rn). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Then, ", "raw": "Then, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\naccelerate launch -m axolotl.cli.train examples/llama-2/qlora.yml\n```", "href": null, "resource": null, "url": null, "code": "accelerate launch -m axolotl.cli.train examples/llama-2/qlora.yml", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Will allow to start the finetuning process on structure defined strictly by you. After finetuning, model will be saved in path provided in config, and you can check out if it performs better than the base one. Or even you can put it on llm Leaderboard to check if we do not have new SOTA :)", "raw": "Will allow to start the finetuning process on structure defined strictly by you. After finetuning, model will be saved in path provided in config, and you can check out if it performs better than the base one. Or even you can put it on llm Leaderboard to check if we do not have new SOTA :)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Have fun and have a great day <3", "raw": "Have fun and have a great day <3", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

[]

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[ { "type": "text", "value": "🔍 Today's pick in Interpretability & Analysis of LMs: Towards Best Practices of Activation Patching in Language Models: Metrics and Methods by ", "raw": "🔍 Today's pick in Interpretability & Analysis of LMs: Towards Best Practices of Activation Patching in Language Models: Metrics and Methods by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@m0pp11", "href": null, "resource": null, "url": null, "code": null, "user": "m0pp11", "label": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@NeelNanda", "href": null, "resource": null, "url": null, "code": null, "user": "NeelNanda", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This work systematically examines the impact of methodological details in activation patching, a popular technique with causal guarantees to quantify the importance of model components in driving model predictions. Authors' recommendations include 1) using in-distribution counterfactual prompts instead of noise/zeroing to mitigate the OOD problem, 2) using logins instead of probabilities as evaluation metrics to enable the discovery of model components with negative influence on predictions, 3) accounting for interaction factors across layers when performing multi-layer patching; and 4) experiment with corrupting different prompt tokens to verify their agreement in the resulting discovered circuits.", "raw": "This work systematically examines the impact of methodological details in activation patching, a popular technique with causal guarantees to quantify the importance of model components in driving model predictions. Authors' recommendations include 1) using in-distribution counterfactual prompts instead of noise/zeroing to mitigate the OOD problem, 2) using logins instead of probabilities as evaluation metrics to enable the discovery of model components with negative influence on predictions, 3) accounting for interaction factors across layers when performing multi-layer patching; and 4) experiment with corrupting different prompt tokens to verify their agreement in the resulting discovered circuits.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Paper: ", "raw": "📄 Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2309.16042", "href": null, "resource": { "type": "paper", "id": "2309.16042", "discussionNum": null }, "url": "https://huggingface.co/papers/2309.16042", "code": null, "user": null, "label": "Towards Best Practices of Activation Patching in Language Models:\n Metrics and Methods (2309.16042)", "lang": null } ]

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[]

[ { "type": "text", "value": "Update on the Newsletter of 🤗 Daily Paper", "raw": "Update on the Newsletter of 🤗 Daily Paper", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Automatic Korean translation is integrated. In the newspaper, \"KO\" links appear, and it will bring you to the translated version of full paper. This is done with the following workflow.", "raw": "Automatic Korean translation is integrated. In the newspaper, \"KO\" links appear, and it will bring you to the translated version of full paper. This is done with the following workflow.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. Grasp the list of arXiv IDs from 🤗 Daily Paper API", "raw": "1. Grasp the list of arXiv IDs from 🤗 Daily Paper API", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. Distribute a number of sub-list of arXiv IDs to VMs (possibly spot instances since the job ends shortly)", "raw": "2. Distribute a number of sub-list of arXiv IDs to VMs (possibly spot instances since the job ends shortly)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3. Commit & push the translated paper in HTML to the designated GitHub repository", "raw": "3. Commit & push the translated paper in HTML to the designated GitHub repository", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "4. Newsletter will include the links to the HTML of each paper", "raw": "4. Newsletter will include the links to the HTML of each paper", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Job distribution to a number of VMs are super easily done with [dstack]( ", "raw": "Job distribution to a number of VMs are super easily done with [dstack]( ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://dstack.ai/", "href": "https://dstack.ai/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ), and the translation sub-workflow is done through 1) download PDF of each paper with arxiv-dl package, 2) PDF => text with nougat-ocr package, 3) a custom trained model( ", "raw": " ), and the translation sub-workflow is done through 1) download PDF of each paper with arxiv-dl package, 2) PDF => text with nougat-ocr package, 3) a custom trained model( ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/nlp-with-deeplearning/enko-t5-small-v0", "href": null, "resource": { "type": "model", "id": "nlp-with-deeplearning/enko-t5-small-v0", "discussionNum": null }, "url": "https://huggingface.co/nlp-with-deeplearning/enko-t5-small-v0", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ) in 🤗 transformers to translate the English text into Korean line by line, and 4) reformat the translation into HTML.", "raw": " ) in 🤗 transformers to translate the English text into Korean line by line, and 4) reformat the translation into HTML.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Many people in Korea are not fluent in English but want to learn about new stuff in AI, so they usually use Google Translate or other services. This is why I made this feature for easier and direct access to the SOTA knowledge. ", "raw": "Many people in Korea are not fluent in English but want to learn about new stuff in AI, so they usually use Google Translate or other services. This is why I made this feature for easier and direct access to the SOTA knowledge. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Are there other countries with the similar needs? If so, it would be wonderful to cooperate to support more languages. Please reach out anyone is interested in this.", "raw": "Are there other countries with the similar needs? If so, it would be wonderful to cooperate to support more languages. Please reach out anyone is interested in this.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "PS; I always wanted to show the usefulness of open ML models by building a well working end to end product, and this newsletter shows it by featuring T5ForConditionalGeneration (translation), SOLAR LLM (summarization). ", "raw": "PS; I always wanted to show the usefulness of open ML models by building a well working end to end product, and this newsletter shows it by featuring T5ForConditionalGeneration (translation), SOLAR LLM (summarization). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "if you want to sub to the newsletter", "raw": "if you want to sub to the newsletter", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ": ", "raw": ": ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://groups.google.com/g/hf-daily-paper-newsletter", "href": "https://groups.google.com/g/hf-daily-paper-newsletter", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "if you want to look into the source codes", "raw": "if you want to look into the source codes", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ": ", "raw": ": ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/deep-diver/hf-daily-paper-newsletter", "href": "https://github.com/deep-diver/hf-daily-paper-newsletter", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Migrated all my GPU consuming Spaces to ZERO, it was super easy to do so (add three lines of code and voila!) and the start-up time decreased dramatically as well 💜 ", "raw": "Migrated all my GPU consuming Spaces to ZERO, it was super easy to do so (add three lines of code and voila!) and the start-up time decreased dramatically as well 💜 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[]

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[ { "type": "text", "value": "GPU Poor POV: My storytelling choices of the week", "raw": "GPU Poor POV: My storytelling choices of the week", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Its end of the week, I decided to summarize my observations in community based LLMs and mention few models in specific area which are very interesting and has capability to create some insightful stories despite of its relatively lightweight form.", "raw": "Its end of the week, I decided to summarize my observations in community based LLMs and mention few models in specific area which are very interesting and has capability to create some insightful stories despite of its relatively lightweight form.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I personally did not use LLMs in my daily routine to tasks like function calling, parsing or assist in code writing. What I tried to use for is storytelling, because it always amaze me how different these models comes to different preferred tasks.", "raw": "I personally did not use LLMs in my daily routine to tasks like function calling, parsing or assist in code writing. What I tried to use for is storytelling, because it always amaze me how different these models comes to different preferred tasks.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "How this model are able to generalize the stories and sometimes, how high level of creativity they carry.", "raw": "How this model are able to generalize the stories and sometimes, how high level of creativity they carry.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/BlueNipples/DaringLotus-v2-10.7b", "href": null, "resource": { "type": "model", "id": "BlueNipples/DaringLotus-v2-10.7b", "discussionNum": null }, "url": "https://huggingface.co/BlueNipples/DaringLotus-v2-10.7b", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " its main target is to generate prose. Quoting the author 'It shares it's good prose, and relatively decent coherency, being a little bit more on the side of prose, and a little bit less on the side of coherency. I like this model for generating great prose if I feel like regening a bit. '", "raw": " its main target is to generate prose. Quoting the author 'It shares it's good prose, and relatively decent coherency, being a little bit more on the side of prose, and a little bit less on the side of coherency. I like this model for generating great prose if I feel like regening a bit. '", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/NeuralNovel/Aeryth-7B-v0.1", "href": "https://huggingface.co/NeuralNovel/Aeryth-7B-v0.1", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "great work by ", "raw": "great work by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@NeuralNovel", "href": null, "resource": null, "url": null, "code": null, "user": "NeuralNovel", "label": null, "lang": null }, { "type": "text", "value": " , I really like how flexible this model is, there is no strict focus on a certain role, so definitely worth a try. Would love to hear more about dataset on which was trained, afaik is private rn. best suited for Science Fiction, History & Romance genres due to the training data used.", "raw": " , I really like how flexible this model is, there is no strict focus on a certain role, so definitely worth a try. Would love to hear more about dataset on which was trained, afaik is private rn. best suited for Science Fiction, History & Romance genres due to the training data used.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And the last one for today is ", "raw": "And the last one for today is ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/FPHam/Sydney_Pirate_Mistral_7b", "href": null, "resource": { "type": "model", "id": "FPHam/Sydney_Pirate_Mistral_7b", "discussionNum": null }, "url": "https://huggingface.co/FPHam/Sydney_Pirate_Mistral_7b", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@FPHam", "href": null, "resource": null, "url": null, "code": null, "user": "FPHam", "label": null, "lang": null }, { "type": "text", "value": " work always amaze me how the models are able to stick to provided role. awesome work as always, Ill for sure use this model to generate some interesting stories.", "raw": " work always amaze me how the models are able to stick to provided role. awesome work as always, Ill for sure use this model to generate some interesting stories.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I know that hype train is going fast but as I observe people here on huggingface are creating really creative models which are for sure worth to try. Have a great day <3", "raw": "I know that hype train is going fast but as I observe people here on huggingface are creating really creative models which are for sure worth to try. Have a great day <3", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

[]

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[ { "type": "text", "value": "I've prepared a Google Colab notebook which allows you to play with interpolating between different people using IP-Adapter SDXL Face-ID Plus. ", "raw": "I've prepared a Google Colab notebook which allows you to play with interpolating between different people using IP-Adapter SDXL Face-ID Plus. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\n\n#Prepare a list t of num_of_results values between 0 and 1\nt_space = torch.linspace(0, 1, num_of_results)\nfor t in tqdm(t_space):\n mix_factor = t.item()\n # interpolate between the two face images \n image = (image1 * (1 - mix_factor) + image2 * mix_factor).astype(np.uint8)\n # interpolate between the two face embedding \n faceid_embeds = torch.lerp(faceid_embeds1, faceid_embeds2, t)\n #generate interpolated result\n images = ip_model.generate(prompt=prompt, negative_prompt=negative_prompt, face_image=image, faceid_embeds=faceid_embeds, shortcut=v2, num_samples=2, scale=scale, s_scale=s_scale, guidance_scale=guidance_scale, width=width, height=height, num_inference_steps=steps, seed=seed)\n \n```", "href": null, "resource": null, "url": null, "code": "\n#Prepare a list t of num_of_results values between 0 and 1\nt_space = torch.linspace(0, 1, num_of_results)\nfor t in tqdm(t_space):\n mix_factor = t.item()\n # interpolate between the two face images \n image = (image1 * (1 - mix_factor) + image2 * mix_factor).astype(np.uint8)\n # interpolate between the two face embedding \n faceid_embeds = torch.lerp(faceid_embeds1, faceid_embeds2, t)\n #generate interpolated result\n images = ip_model.generate(prompt=prompt, negative_prompt=negative_prompt, face_image=image, faceid_embeds=faceid_embeds, shortcut=v2, num_samples=2, scale=scale, s_scale=s_scale, guidance_scale=guidance_scale, width=width, height=height, num_inference_steps=steps, seed=seed)\n ", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Link to notebook:", "raw": "Link to notebook:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://colab.research.google.com/#fileId=https%3A//huggingface.co/datasets/Norod78/face_id_v2_test_code/blob/main/norod78_faceid_sdxl_plus_v2_test.ipynb", "href": null, "resource": { "type": "dataset", "id": "Norod78/face_id_v2_test_code", "discussionNum": null }, "url": "https://colab.research.google.com/#fileId=https%3A//huggingface.co/datasets/Norod78/face_id_v2_test_code/blob/main/norod78_faceid_sdxl_plus_v2_test.ipynb", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Link to Face-ID Repo:", "raw": "Link to Face-ID Repo:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/h94/IP-Adapter-FaceID", "href": null, "resource": { "type": "model", "id": "h94/IP-Adapter-FaceID", "discussionNum": null }, "url": "https://huggingface.co/h94/IP-Adapter-FaceID", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Link to all sorts of generated examples (Use the file tab):", "raw": "Link to all sorts of generated examples (Use the file tab):", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/Norod78/face_id_v2_test_code/tree/main/sdxl_plus_v2_outputs", "href": null, "resource": { "type": "dataset", "id": "Norod78/face_id_v2_test_code", "discussionNum": null }, "url": "https://huggingface.co/datasets/Norod78/face_id_v2_test_code/tree/main/sdxl_plus_v2_outputs", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "There's a lot of interest in machine learning models that generate 3D objects, so Gradio now supports previewing STL files natively in the ", "raw": "There's a lot of interest in machine learning models that generate 3D objects, so Gradio now supports previewing STL files natively in the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`Model3D`", "href": null, "resource": null, "url": null, "code": "Model3D", "user": null, "label": null, "lang": null }, { "type": "text", "value": " component. Huge thanks to ", "raw": " component. Huge thanks to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`Monius`", "href": null, "resource": null, "url": null, "code": "Monius", "user": null, "label": null, "lang": null }, { "type": "text", "value": " for the contribution 🔥🔥", "raw": " for the contribution 🔥🔥", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🌳 Model Family Tree", "raw": "🌳 Model Family Tree", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Merging models has become a powerful way to compress information and build powerful models for cheap. Right now, the process is still quite experimental: which models to merge? which parameters should I use? We have some intuition but no principled approach.", "raw": "Merging models has become a powerful way to compress information and build powerful models for cheap. Right now, the process is still quite experimental: which models to merge? which parameters should I use? We have some intuition but no principled approach.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I made a little tool to make things a little clearer. It allows you to visualize the family tree of any model on the Hub. It also displays the type of license they use: permissive (green), noncommercial (red), and unknown (gray). It should help people select the right license based on the parent models.", "raw": "I made a little tool to make things a little clearer. It allows you to visualize the family tree of any model on the Hub. It also displays the type of license they use: permissive (green), noncommercial (red), and unknown (gray). It should help people select the right license based on the parent models.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In addition, I hope it can be refined to extract more information about these models: do models from very different branches work better when merged? Can we select them based on the weight difference? There are a lot of questions to explore in this new space. :)", "raw": "In addition, I hope it can be refined to extract more information about these models: do models from very different branches work better when merged? Can we select them based on the weight difference? There are a lot of questions to explore in this new space. :)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here's a link to the colab notebook I made: ", "raw": "Here's a link to the colab notebook I made: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/1s2eQlolcI1VGgDhqWIANfkfKvcKrMyNr", "href": "https://colab.research.google.com/drive/1s2eQlolcI1VGgDhqWIANfkfKvcKrMyNr", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If you want to know more about model merging or build you own merges, here's the article I wrote about this topic: ", "raw": "If you want to know more about model merging or build you own merges, here's the article I wrote about this topic: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/mlabonne/merge-models", "href": "https://huggingface.co/blog/mlabonne/merge-models", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "GPU Poor POV: Low Hanging Fruits", "raw": "GPU Poor POV: Low Hanging Fruits", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Sometimes we had to work with different language than English (what a surprise!) and it can be problematic, because as you may know many algorithms are mainly developed in English. ", "raw": "Sometimes we had to work with different language than English (what a surprise!) and it can be problematic, because as you may know many algorithms are mainly developed in English. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I was involved in building RAG in Polish language. At first, we need an proper embeddings for Polish language to feed them into lightweight LLM. ", "raw": "I was involved in building RAG in Polish language. At first, we need an proper embeddings for Polish language to feed them into lightweight LLM. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Looking through possible solution I become aware that existing/possible models are not accurate enough, and worked much worse than its 'english equivalent'. ", "raw": "Looking through possible solution I become aware that existing/possible models are not accurate enough, and worked much worse than its 'english equivalent'. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "First thing that comes to mind is:", "raw": "First thing that comes to mind is:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\n Lets become a mad scientist, download all possible data and train model for months to get the proper one. \n```", "href": null, "resource": null, "url": null, "code": " Lets become a mad scientist, download all possible data and train model for months to get the proper one. ", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "But there are few cons of this. ", "raw": "But there are few cons of this. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Its computionally heavy", "raw": "- Its computionally heavy", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- You are not full time researcher ", "raw": "- You are not full time researcher ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- you have potential clients who want to use your solution, and they really happy to use it (in optimistic mood). ", "raw": "- you have potential clients who want to use your solution, and they really happy to use it (in optimistic mood). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here comes the low hanging fruits.", "raw": "Here comes the low hanging fruits.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " We developed a easier, workable solution. Instead of training new SOTA, we can use translation module like this one:", "raw": " We developed a easier, workable solution. Instead of training new SOTA, we can use translation module like this one:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Helsinki-NLP/opus-mt-pl-en", "href": null, "resource": { "type": "model", "id": "Helsinki-NLP/opus-mt-pl-en", "discussionNum": null }, "url": "https://huggingface.co/Helsinki-NLP/opus-mt-pl-en", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "translate your knowledge base to english, and use proper embedding model accurately. ", "raw": "translate your knowledge base to english, and use proper embedding model accurately. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I converted existing model using ctranslate2, ", "raw": "I converted existing model using ctranslate2, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\nct2-transformers-converter --model Helsinki-NLP/opus-mt-pl-en --output_dir opus-mt-pl-en\n```", "href": null, "resource": null, "url": null, "code": "ct2-transformers-converter --model Helsinki-NLP/opus-mt-pl-en --output_dir opus-mt-pl-en", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "so making an inference is not heavy (we observe 5 times speedup in compare to original version).", "raw": "so making an inference is not heavy (we observe 5 times speedup in compare to original version).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And by indexing knowledge base, we can return answer to LLM in any language. (Indexes of context found in english language are equal to indexes in native language knowledge base). ", "raw": "And by indexing knowledge base, we can return answer to LLM in any language. (Indexes of context found in english language are equal to indexes in native language knowledge base). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Of course there are some tweaks required, we have to validate accuracy of the translation.", "raw": "Of course there are some tweaks required, we have to validate accuracy of the translation.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It was nice episode, we have our work done, there are people who can use it, so additive value exists. ", "raw": "It was nice episode, we have our work done, there are people who can use it, so additive value exists. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Have a great day and I wish you more effective deploys! <3", "raw": "Have a great day and I wish you more effective deploys! <3", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Explaining the 👑 of zero-shot open-vocabulary object detection: OWLv2 🦉", "raw": "Explaining the 👑 of zero-shot open-vocabulary object detection: OWLv2 🦉", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "OWLv2 is scaled version of a model called OWL-ViT, so let's take a look at that first. 📝", "raw": "OWLv2 is scaled version of a model called OWL-ViT, so let's take a look at that first. 📝", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "OWLViT is an open vocabulary object detector, meaning, it can detect objects it didn't explicitly see during the training. 👀", "raw": "OWLViT is an open vocabulary object detector, meaning, it can detect objects it didn't explicitly see during the training. 👀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What's cool is that it can take both image and text queries! This is thanks to how the image and text features aren't fused together.", "raw": "What's cool is that it can take both image and text queries! This is thanks to how the image and text features aren't fused together.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Taking a look at the architecture, the authors firstly do contrastive pre-training of a vision and a text encoder (just like CLIP). They take that model, remove the final pooling layer and attach a lightweight classification and box detection head and fine-tune.", "raw": "Taking a look at the architecture, the authors firstly do contrastive pre-training of a vision and a text encoder (just like CLIP). They take that model, remove the final pooling layer and attach a lightweight classification and box detection head and fine-tune.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "During fine-tuning for object detection, they calculate the loss over bipartite matches. Simply put, loss is calculated over the predicted objects against ground truth objects and the goal is to find a perfect match of these two sets where each object is matched to one object in ground truth. ", "raw": "During fine-tuning for object detection, they calculate the loss over bipartite matches. Simply put, loss is calculated over the predicted objects against ground truth objects and the goal is to find a perfect match of these two sets where each object is matched to one object in ground truth. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "OWL-ViT is very scalable. You can easily scale most language models or vision-language models because they require no supervision, but this isn't the case for object detection: you still need weak supervision. Moreover, only scaling the encoders creates a bottleneck after a while.", "raw": "OWL-ViT is very scalable. You can easily scale most language models or vision-language models because they require no supervision, but this isn't the case for object detection: you still need weak supervision. Moreover, only scaling the encoders creates a bottleneck after a while.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The authors wanted to scale OWL-ViT with more data, so they used OWL-ViT for labelling to train a better detector, \"self-train\" a new detector on the labels, and fine-tune the model on human-annotated data. ", "raw": "The authors wanted to scale OWL-ViT with more data, so they used OWL-ViT for labelling to train a better detector, \"self-train\" a new detector on the labels, and fine-tune the model on human-annotated data. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Thanks to this, OWLv2 scaled very well and topped leaderboards on open vocabulary object detection 👑 ", "raw": "Thanks to this, OWLv2 scaled very well and topped leaderboards on open vocabulary object detection 👑 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If you'd like to try it out, I will leave couple of links with apps, notebooks and more in the comments! 🤗 ", "raw": "If you'd like to try it out, I will leave couple of links with apps, notebooks and more in the comments! 🤗 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "First post!", "raw": "First post!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Very cool feature.", "raw": "Very cool feature.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🔍 Today's pick in Interpretability & Analysis of LMs: Can Large Language Models Explain Themselves? by ", "raw": "🔍 Today's pick in Interpretability & Analysis of LMs: Can Large Language Models Explain Themselves? by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@andreasmadsen", "href": null, "resource": null, "url": null, "code": null, "user": "andreasmadsen", "label": null, "lang": null }, { "type": "text", "value": " Sarath Chandar & ", "raw": " Sarath Chandar & ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@sivareddyg", "href": null, "resource": null, "url": null, "code": null, "user": "sivareddyg", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "LLMs can provide wrong but convincing explanations for their behavior, and this might lead to ill-placed confidence in their predictions. This study uses self-consistency checks to measure the faithfulness of LLM explanations: if an LLM says a set of words is important for making a prediction, then it should not be able to make the same prediction without these words. Results demonstrate that LLM self-explanations faithfulness of self-explanations cannot be reliably trusted, as they prove to be very task and model dependent, with bigger model generally producing more faithful explanations.", "raw": "LLMs can provide wrong but convincing explanations for their behavior, and this might lead to ill-placed confidence in their predictions. This study uses self-consistency checks to measure the faithfulness of LLM explanations: if an LLM says a set of words is important for making a prediction, then it should not be able to make the same prediction without these words. Results demonstrate that LLM self-explanations faithfulness of self-explanations cannot be reliably trusted, as they prove to be very task and model dependent, with bigger model generally producing more faithful explanations.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Paper: ", "raw": "📄 Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.07927", "href": null, "resource": { "type": "paper", "id": "2401.07927", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.07927", "code": null, "user": null, "label": "Can Large Language Models Explain Themselves? (2401.07927)", "lang": null } ]

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[ { "type": "text", "value": "Self-Rewarding Language Models", "raw": "Self-Rewarding Language Models", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "paper page: ", "raw": "paper page: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.10020", "href": null, "resource": { "type": "paper", "id": "2401.10020", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.10020", "code": null, "user": null, "label": "Self-Rewarding Language Models (2401.10020)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Fine-tuning Llama 2 70B on three iterations of our approach yields a model that outperforms many existing systems on the AlpacaEval 2.0 leaderboard, including Claude 2, Gemini Pro, and GPT-4 0613", "raw": "Fine-tuning Llama 2 70B on three iterations of our approach yields a model that outperforms many existing systems on the AlpacaEval 2.0 leaderboard, including Claude 2, Gemini Pro, and GPT-4 0613", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Last week, we released 🤗 Transformers.js v2.14, which added support for SAM (Segment Anything Model).", "raw": "Last week, we released 🤗 Transformers.js v2.14, which added support for SAM (Segment Anything Model).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This means you can now generate high-quality segmentation masks for objects in a scene, directly in your browser! 🤯", "raw": "This means you can now generate high-quality segmentation masks for objects in a scene, directly in your browser! 🤯", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Demo (+ source code): ", "raw": "Demo (+ source code): ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Xenova/segment-anything-web", "href": null, "resource": { "type": "space", "id": "Xenova/segment-anything-web", "discussionNum": null }, "url": "https://huggingface.co/spaces/Xenova/segment-anything-web", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Xenova/slimsam-77-uniform", "href": null, "resource": { "type": "model", "id": "Xenova/slimsam-77-uniform", "discussionNum": null }, "url": "https://huggingface.co/Xenova/slimsam-77-uniform", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "But how does this differ from Meta's original demo? 🤔 Didn't that also run in-browser?", "raw": "But how does this differ from Meta's original demo? 🤔 Didn't that also run in-browser?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Well, in their demo, the image embeddings are computed server-side, then sent to the client for decoding. Trying to do this all client-side would be completely impractical: taking minutes per image! 😵‍💫", "raw": "Well, in their demo, the image embeddings are computed server-side, then sent to the client for decoding. Trying to do this all client-side would be completely impractical: taking minutes per image! 😵‍💫", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "That's where SlimSAM comes to the rescue! SlimSAM is a novel SAM compression method, able to shrink the model over 100x (637M → 5.5M params), while still achieving remarkable results!", "raw": "That's where SlimSAM comes to the rescue! SlimSAM is a novel SAM compression method, able to shrink the model over 100x (637M → 5.5M params), while still achieving remarkable results!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The best part? You can get started in a few lines of JavaScript code, thanks to Transformers.js! 🔥", "raw": "The best part? You can get started in a few lines of JavaScript code, thanks to Transformers.js! 🔥", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\n// npm i @xenova/transformers\nimport { SamModel, AutoProcessor, RawImage } from '@xenova/transformers';\n\n// Load model and processor\nconst model = await SamModel.from_pretrained('Xenova/slimsam-77-uniform');\nconst processor = await AutoProcessor.from_pretrained('Xenova/slimsam-77-uniform');\n\n// Prepare image and input points\nconst img_url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/corgi.jpg';\nconst raw_image = await RawImage.read(img_url);\nconst input_points = [[[340, 250]]];\n\n// Process inputs and perform mask generation\nconst inputs = await processor(raw_image, input_points);\nconst outputs = await model(inputs);\n\n// Post-process masks\nconst masks = await processor.post_process_masks(outputs.pred_masks, inputs.original_sizes, inputs.reshaped_input_sizes);\nconsole.log(masks);\n\n// Visualize the mask\nconst image = RawImage.fromTensor(masks[0][0].mul(255));\nimage.save('mask.png');\n```", "href": null, "resource": null, "url": null, "code": "// npm i @xenova/transformers\nimport { SamModel, AutoProcessor, RawImage } from '@xenova/transformers';\n\n// Load model and processor\nconst model = await SamModel.from_pretrained('Xenova/slimsam-77-uniform');\nconst processor = await AutoProcessor.from_pretrained('Xenova/slimsam-77-uniform');\n\n// Prepare image and input points\nconst img_url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/corgi.jpg';\nconst raw_image = await RawImage.read(img_url);\nconst input_points = [[[340, 250]]];\n\n// Process inputs and perform mask generation\nconst inputs = await processor(raw_image, input_points);\nconst outputs = await model(inputs);\n\n// Post-process masks\nconst masks = await processor.post_process_masks(outputs.pred_masks, inputs.original_sizes, inputs.reshaped_input_sizes);\nconsole.log(masks);\n\n// Visualize the mask\nconst image = RawImage.fromTensor(masks[0][0].mul(255));\nimage.save('mask.png');", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I can't wait to see what you build with it! 🤗", "raw": "I can't wait to see what you build with it! 🤗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Posting about a very underrated model that tops paperswithcode across different segmentation benchmarks: OneFormer 👑", "raw": "Posting about a very underrated model that tops paperswithcode across different segmentation benchmarks: OneFormer 👑", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "OneFormer is a \"truly universal\" model for semantic, instance and panoptic segmentation tasks ⚔️ ", "raw": "OneFormer is a \"truly universal\" model for semantic, instance and panoptic segmentation tasks ⚔️ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What makes is truly universal is that it's a single model that is trained only once and can be used across all tasks.", "raw": "What makes is truly universal is that it's a single model that is trained only once and can be used across all tasks.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The enabler here is the text conditioning, i.e. the model is given a text query that states task type along with the appropriate input, and using contrastive loss, the model learns the difference between different task types 👇 (see in the image below) ", "raw": "The enabler here is the text conditioning, i.e. the model is given a text query that states task type along with the appropriate input, and using contrastive loss, the model learns the difference between different task types 👇 (see in the image below) ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It's also super easy to use with transformers. ", "raw": "It's also super easy to use with transformers. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```python\nfrom transformers import OneFormerProcessor, OneFormerForUniversalSegmentation\n\nprocessor = OneFormerProcessor.from_pretrained(\"shi-labs/oneformer_ade20k_swin_large\")\nmodel = OneFormerForUniversalSegmentation.from_pretrained(\"shi-labs/oneformer_ade20k_swin_large\")\n\n# swap the postprocessing and task_inputs for different types of segmentation\nsemantic_inputs = processor(images=image, task_inputs=[\"semantic\"], return_tensors=\"pt\")\nsemantic_outputs = model(**semantic_inputs)\npredicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\n```", "href": null, "resource": null, "url": null, "code": "from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation\n\nprocessor = OneFormerProcessor.from_pretrained(\"shi-labs/oneformer_ade20k_swin_large\")\nmodel = OneFormerForUniversalSegmentation.from_pretrained(\"shi-labs/oneformer_ade20k_swin_large\")\n\n# swap the postprocessing and task_inputs for different types of segmentation\nsemantic_inputs = processor(images=image, task_inputs=[\"semantic\"], return_tensors=\"pt\")\nsemantic_outputs = model(**semantic_inputs)\npredicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]", "user": null, "label": null, "lang": "python" }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I have drafted a notebook for you to try right away ✨ ", "raw": "I have drafted a notebook for you to try right away ✨ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/1wfJhoTFqUqcTAYAOUc6TXUubBTmOYaVa?usp=sharing", "href": "https://colab.research.google.com/drive/1wfJhoTFqUqcTAYAOUc6TXUubBTmOYaVa?usp=sharing", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can also check out the Space without checking out the code itself 👉 ", "raw": "You can also check out the Space without checking out the code itself 👉 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/shi-labs/OneFormer", "href": null, "resource": { "type": "space", "id": "shi-labs/OneFormer", "discussionNum": null }, "url": "https://huggingface.co/spaces/shi-labs/OneFormer", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model", "raw": "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "paper page: ", "raw": "paper page: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.09417", "href": null, "resource": { "type": "paper", "id": "2401.09417", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.09417", "code": null, "user": null, "label": "Vision Mamba: Efficient Visual Representation Learning with\n Bidirectional State Space Model (2401.09417)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Recently the state space models (SSMs) with efficient hardware-aware designs, i.e., Mamba, have shown great potential for long sequence modeling. Building efficient and generic vision backbones purely upon SSMs is an appealing direction. However, representing visual data is challenging for SSMs due to the position-sensitivity of visual data and the requirement of global context for visual understanding. In this paper, we show that the reliance of visual representation learning on self-attention is not necessary and propose a new generic vision backbone with bidirectional Mamba blocks (Vim), which marks the image sequences with position embeddings and compresses the visual representation with bidirectional state space models. On ImageNet classification, COCO object detection, and ADE20k semantic segmentation tasks, Vim achieves higher performance compared to well-established vision transformers like DeiT, while also demonstrating significantly improved computation & memory efficiency. For example, Vim is 2.8times faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on images with a resolution of 1248times1248. The results demonstrate that Vim is capable of overcoming the computation & memory constraints on performing Transformer-style understanding for high-resolution images and it has great potential to become the next-generation backbone for vision foundation models.", "raw": "Recently the state space models (SSMs) with efficient hardware-aware designs, i.e., Mamba, have shown great potential for long sequence modeling. Building efficient and generic vision backbones purely upon SSMs is an appealing direction. However, representing visual data is challenging for SSMs due to the position-sensitivity of visual data and the requirement of global context for visual understanding. In this paper, we show that the reliance of visual representation learning on self-attention is not necessary and propose a new generic vision backbone with bidirectional Mamba blocks (Vim), which marks the image sequences with position embeddings and compresses the visual representation with bidirectional state space models. On ImageNet classification, COCO object detection, and ADE20k semantic segmentation tasks, Vim achieves higher performance compared to well-established vision transformers like DeiT, while also demonstrating significantly improved computation & memory efficiency. For example, Vim is 2.8times faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on images with a resolution of 1248times1248. The results demonstrate that Vim is capable of overcoming the computation & memory constraints on performing Transformer-style understanding for high-resolution images and it has great potential to become the next-generation backbone for vision foundation models.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Most upvoted papers of 2023 on HF. What do you think are going to be the most prominent research topics in AI for 2024 (also, don't forget to add your papers to the hub this year!). ", "raw": "Most upvoted papers of 2023 on HF. What do you think are going to be the most prominent research topics in AI for 2024 (also, don't forget to add your papers to the hub this year!). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "From: ", "raw": "From: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/hysts/daily-papers", "href": null, "resource": { "type": "space", "id": "hysts/daily-papers", "discussionNum": null }, "url": "https://huggingface.co/spaces/hysts/daily-papers", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "💥 Today's pick in Interpretability & Analysis of LMs: 🩺 Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models by ", "raw": "💥 Today's pick in Interpretability & Analysis of LMs: 🩺 Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@asmadotgh", "href": null, "resource": null, "url": null, "code": null, "user": "asmadotgh", "label": null, "lang": null }, { "type": "text", "value": ", ", "raw": ", ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@codevan", "href": null, "resource": null, "url": null, "code": null, "user": "codevan", "label": null, "lang": null }, { "type": "text", "value": ", ", "raw": ", ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@1wheel", "href": null, "resource": null, "url": null, "code": null, "user": "1wheel", "label": null, "lang": null }, { "type": "text", "value": ", ", "raw": ", ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@iislucas", "href": null, "resource": null, "url": null, "code": null, "user": "iislucas", "label": null, "lang": null }, { "type": "text", "value": " & ", "raw": " & ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@mega", "href": null, "resource": null, "url": null, "code": null, "user": "mega", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Patchscopes is a generalized framework for verbalizing information contained in LM representations. This is achieved via a mid-forward patching operation inserting the information into an ad-hoc prompt aimed at eliciting model knowledge. Patchscope instances for vocabulary projection, feature extraction and entity resolution in model representation are show to outperform popular interpretability approaches, often resulting in more robust and expressive information.", "raw": "Patchscopes is a generalized framework for verbalizing information contained in LM representations. This is achieved via a mid-forward patching operation inserting the information into an ad-hoc prompt aimed at eliciting model knowledge. Patchscope instances for vocabulary projection, feature extraction and entity resolution in model representation are show to outperform popular interpretability approaches, often resulting in more robust and expressive information.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🌐 Website: ", "raw": "🌐 Website: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://pair-code.github.io/interpretability/patchscopes/", "href": "https://pair-code.github.io/interpretability/patchscopes/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Paper: ", "raw": "📄 Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.06102", "href": null, "resource": { "type": "paper", "id": "2401.06102", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.06102", "code": null, "user": null, "label": "Patchscope: A Unifying Framework for Inspecting Hidden Representations\n of Language Models (2401.06102)", "lang": null } ]

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[]

[ { "type": "text", "value": "✌🏼Two new models dropped today 👇🏽", "raw": "✌🏼Two new models dropped today 👇🏽", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1) 👩🏾‍💻 𝐃𝐞𝐜𝐢𝐂𝐨𝐝𝐞𝐫-𝟔𝐁", "raw": "1) 👩🏾‍💻 𝐃𝐞𝐜𝐢𝐂𝐨𝐝𝐞𝐫-𝟔𝐁", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "👉🏽 Supports 𝟖 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞𝐬: C, C# C++, GO, Rust, Python, Java, and Javascript.", "raw": "👉🏽 Supports 𝟖 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞𝐬: C, C# C++, GO, Rust, Python, Java, and Javascript.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "👉🏽 Released under the 𝐀𝐩𝐚𝐜𝐡𝐞 𝟐.𝟎 𝐥𝐢𝐜𝐞𝐧𝐬𝐞", "raw": "👉🏽 Released under the 𝐀𝐩𝐚𝐜𝐡𝐞 𝟐.𝟎 𝐥𝐢𝐜𝐞𝐧𝐬𝐞", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🥊 𝐏𝐮𝐧𝐜𝐡𝐞𝐬 𝐚𝐛𝐨𝐯𝐞 𝐢𝐭𝐬 𝐰𝐞𝐢𝐠𝐡𝐭 𝐜𝐥𝐚𝐬𝐬 𝐨𝐧 𝐇𝐮𝐦𝐚𝐧𝐄𝐯𝐚𝐥: Beats out CodeGen 2.5 7B and StarCoder 7B on most supported languages. Has a 3-point lead over StarCoderBase 15.5B for Python", "raw": "🥊 𝐏𝐮𝐧𝐜𝐡𝐞𝐬 𝐚𝐛𝐨𝐯𝐞 𝐢𝐭𝐬 𝐰𝐞𝐢𝐠𝐡𝐭 𝐜𝐥𝐚𝐬𝐬 𝐨𝐧 𝐇𝐮𝐦𝐚𝐧𝐄𝐯𝐚𝐥: Beats out CodeGen 2.5 7B and StarCoder 7B on most supported languages. Has a 3-point lead over StarCoderBase 15.5B for Python", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💻 𝑻𝒓𝒚 𝒊𝒕 𝒐𝒖𝒕:", "raw": "💻 𝑻𝒓𝒚 𝒊𝒕 𝒐𝒖𝒕:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🃏 𝐌𝐨𝐝𝐞𝐥 𝐂𝐚𝐫𝐝: ", "raw": "🃏 𝐌𝐨𝐝𝐞𝐥 𝐂𝐚𝐫𝐝: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Deci/DeciCoder-6B", "href": null, "resource": { "type": "model", "id": "Deci/DeciCoder-6B", "discussionNum": null }, "url": "https://huggingface.co/Deci/DeciCoder-6B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📓 𝐍𝐨𝐭𝐞𝐛𝐨𝐨𝐤: ", "raw": "📓 𝐍𝐨𝐭𝐞𝐛𝐨𝐨𝐤: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/1QRbuser0rfUiFmQbesQJLXVtBYZOlKpB", "href": "https://colab.research.google.com/drive/1QRbuser0rfUiFmQbesQJLXVtBYZOlKpB", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🪧 𝐇𝐮𝐠𝐠𝐢𝐧𝐠𝐅𝐚𝐜𝐞 𝐒𝐩𝐚𝐜𝐞: ", "raw": "🪧 𝐇𝐮𝐠𝐠𝐢𝐧𝐠𝐅𝐚𝐜𝐞 𝐒𝐩𝐚𝐜𝐞: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Deci/DeciCoder-6B-Demo", "href": null, "resource": { "type": "space", "id": "Deci/DeciCoder-6B-Demo", "discussionNum": null }, "url": "https://huggingface.co/spaces/Deci/DeciCoder-6B-Demo", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2) 🎨 𝐃𝐞𝐜𝐢𝐃𝐢𝐟𝐟𝐮𝐬𝐢𝐨𝐧 𝐯𝟐.𝟎", "raw": "2) 🎨 𝐃𝐞𝐜𝐢𝐃𝐢𝐟𝐟𝐮𝐬𝐢𝐨𝐧 𝐯𝟐.𝟎", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "👉🏽 Produces quality images on par with Stable Diffusion v1.5, but 𝟐.𝟔 𝐭𝐢𝐦𝐞𝐬 𝐟𝐚𝐬𝐭𝐞𝐫 𝐢𝐧 𝟒𝟎% 𝐟𝐞𝐰𝐞𝐫 𝐢𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬", "raw": "👉🏽 Produces quality images on par with Stable Diffusion v1.5, but 𝟐.𝟔 𝐭𝐢𝐦𝐞𝐬 𝐟𝐚𝐬𝐭𝐞𝐫 𝐢𝐧 𝟒𝟎% 𝐟𝐞𝐰𝐞𝐫 𝐢𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "👉🏽 Employs a 𝐬𝐦𝐚𝐥𝐥𝐞𝐫 𝐚𝐧𝐝 𝐟𝐚𝐬𝐭𝐞𝐫 𝐔-𝐍𝐞𝐭 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭 𝐰𝐡𝐢𝐜𝐡 𝐡𝐚𝐬 𝟖𝟔𝟎 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬.", "raw": "👉🏽 Employs a 𝐬𝐦𝐚𝐥𝐥𝐞𝐫 𝐚𝐧𝐝 𝐟𝐚𝐬𝐭𝐞𝐫 𝐔-𝐍𝐞𝐭 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭 𝐰𝐡𝐢𝐜𝐡 𝐡𝐚𝐬 𝟖𝟔𝟎 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "👉🏽 Uses an optimized scheduler, 𝐒𝐪𝐮𝐞𝐞𝐳𝐞𝐝𝐃𝐏𝐌++, which 𝐜𝐮𝐭𝐬 𝐝𝐨𝐰𝐧 𝐭𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐬𝐭𝐞𝐩𝐬 𝐧𝐞𝐞𝐝𝐞𝐝 𝐭𝐨 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐢𝐦𝐚𝐠𝐞 𝐟𝐫𝐨𝐦 𝟏𝟔 𝐭𝐨 𝟏𝟎.", "raw": "👉🏽 Uses an optimized scheduler, 𝐒𝐪𝐮𝐞𝐞𝐳𝐞𝐝𝐃𝐏𝐌++, which 𝐜𝐮𝐭𝐬 𝐝𝐨𝐰𝐧 𝐭𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐬𝐭𝐞𝐩𝐬 𝐧𝐞𝐞𝐝𝐞𝐝 𝐭𝐨 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐢𝐦𝐚𝐠𝐞 𝐟𝐫𝐨𝐦 𝟏𝟔 𝐭𝐨 𝟏𝟎.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "👉🏽 Released under the 𝐂𝐫𝐞𝐚𝐭𝐢𝐯𝐞𝐌𝐋 𝐎𝐩𝐞𝐧 𝐑𝐀𝐈𝐋++-𝐌 𝐋𝐢𝐜𝐞𝐧𝐬𝐞.", "raw": "👉🏽 Released under the 𝐂𝐫𝐞𝐚𝐭𝐢𝐯𝐞𝐌𝐋 𝐎𝐩𝐞𝐧 𝐑𝐀𝐈𝐋++-𝐌 𝐋𝐢𝐜𝐞𝐧𝐬𝐞.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💻 𝑻𝒓𝒚 𝒊𝒕 𝒐𝒖𝒕:", "raw": "💻 𝑻𝒓𝒚 𝒊𝒕 𝒐𝒖𝒕:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🃏 𝐌𝐨𝐝𝐞𝐥 𝐂𝐚𝐫𝐝: ", "raw": "🃏 𝐌𝐨𝐝𝐞𝐥 𝐂𝐚𝐫𝐝: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Deci/DeciDiffusion-v2-0", "href": null, "resource": { "type": "model", "id": "Deci/DeciDiffusion-v2-0", "discussionNum": null }, "url": "https://huggingface.co/Deci/DeciDiffusion-v2-0", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📓 𝐍𝐨𝐭𝐞𝐛𝐨𝐨𝐤: ", "raw": "📓 𝐍𝐨𝐭𝐞𝐛𝐨𝐨𝐤: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/11Ui_KRtK2DkLHLrW0aa11MiDciW4dTuB", "href": "https://colab.research.google.com/drive/11Ui_KRtK2DkLHLrW0aa11MiDciW4dTuB", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🪧 𝐇𝐮𝐠𝐠𝐢𝐧𝐠𝐅𝐚𝐜𝐞 𝐒𝐩𝐚𝐜𝐞: ", "raw": "🪧 𝐇𝐮𝐠𝐠𝐢𝐧𝐠𝐅𝐚𝐜𝐞 𝐒𝐩𝐚𝐜𝐞: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Deci/DeciDiffusion-v2-0", "href": null, "resource": { "type": "space", "id": "Deci/DeciDiffusion-v2-0", "discussionNum": null }, "url": "https://huggingface.co/spaces/Deci/DeciDiffusion-v2-0", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Help support the projects by liking the model cards and the spaces!", "raw": "Help support the projects by liking the model cards and the spaces!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Cheers and happy hacking!", "raw": "Cheers and happy hacking!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "𝐄𝐦𝐛𝐫𝐚𝐜𝐞𝐝 𝐛𝐲 𝐇𝐮𝐠𝐠𝐢𝐧𝐠 𝐅𝐚𝐜𝐞: 𝐭𝐡𝐞 𝐈𝐧𝐬𝐢𝐝𝐞 𝐒𝐭𝐨𝐫𝐲 𝐨𝐟 𝐎𝐮𝐫 𝐒𝐭𝐚𝐫𝐭𝐮𝐩’𝐬 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧", "raw": "𝐄𝐦𝐛𝐫𝐚𝐜𝐞𝐝 𝐛𝐲 𝐇𝐮𝐠𝐠𝐢𝐧𝐠 𝐅𝐚𝐜𝐞: 𝐭𝐡𝐞 𝐈𝐧𝐬𝐢𝐝𝐞 𝐒𝐭𝐨𝐫𝐲 𝐨𝐟 𝐎𝐮𝐫 𝐒𝐭𝐚𝐫𝐭𝐮𝐩’𝐬 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In late 2021, our team of five engineers, scattered around the globe, signed the papers to shut down our startup, Gradio. For many founders, this would have been a moment of sadness or even bitter reflection.", "raw": "In late 2021, our team of five engineers, scattered around the globe, signed the papers to shut down our startup, Gradio. For many founders, this would have been a moment of sadness or even bitter reflection.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "But we were celebrating. We were getting acquired by Hugging Face! ", "raw": "But we were celebrating. We were getting acquired by Hugging Face! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We had been working very hard towards this acquisition, but for weeks, the acquisition had been blocked by a single investor. The more we pressed him, the more he buckled down, refusing to sign off on the acquisition. Until, unexpectedly, the investor conceded, allowing us to join Hugging Face.", "raw": "We had been working very hard towards this acquisition, but for weeks, the acquisition had been blocked by a single investor. The more we pressed him, the more he buckled down, refusing to sign off on the acquisition. Until, unexpectedly, the investor conceded, allowing us to join Hugging Face.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For the first time since our acquisition, I’m writing down the story in detail, hoping that it may shed some light into the obscure world of startup acquisitions and what decisions founders can make to improve their odds for a successful acquisition.", "raw": "For the first time since our acquisition, I’m writing down the story in detail, hoping that it may shed some light into the obscure world of startup acquisitions and what decisions founders can make to improve their odds for a successful acquisition.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "To understand how we got acquired by Hugging Face, you need to know why we started Gradio.", "raw": "To understand how we got acquired by Hugging Face, you need to know why we started Gradio.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "𝐀𝐧 𝐈𝐝𝐞𝐚 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐇𝐞𝐚𝐫𝐭", "raw": "𝐀𝐧 𝐈𝐝𝐞𝐚 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐇𝐞𝐚𝐫𝐭", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Two years before the acquisition, in early 2019, I was working on a research project at Stanford. It was the third year of my PhD, and my labmates and I had trained a machine learning model that could predict patient biomarkers (such as whether patients had certain diseases or an implanted pacemaker) from an ultrasound image of their heart — as well as a cardiologist. ", "raw": "Two years before the acquisition, in early 2019, I was working on a research project at Stanford. It was the third year of my PhD, and my labmates and I had trained a machine learning model that could predict patient biomarkers (such as whether patients had certain diseases or an implanted pacemaker) from an ultrasound image of their heart — as well as a cardiologist. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Naturally, cardiologists were skeptical... read the rest of the story here: ", "raw": "Naturally, cardiologists were skeptical... read the rest of the story here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://twitter.com/abidlabs/status/1745533306492588303", "href": "https://twitter.com/abidlabs/status/1745533306492588303", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🪟32k-context BERT for embedding and RAG on long corpus", "raw": "🪟32k-context BERT for embedding and RAG on long corpus", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Monarch Mixer is a new architecture to enable long context BERT for large corpus and can be fine-tuned for large context retrieval.", "raw": "Monarch Mixer is a new architecture to enable long context BERT for large corpus and can be fine-tuned for large context retrieval.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Quite interesting and important as BERT is still the most used LLM in production for \"old school\" tasks like classification, NER, embeddings, but is also a key component for RAG.", "raw": "Quite interesting and important as BERT is still the most used LLM in production for \"old school\" tasks like classification, NER, embeddings, but is also a key component for RAG.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2310.12109", "href": "https://arxiv.org/abs/2310.12109", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Blog: ", "raw": "Blog: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://hazyresearch.stanford.edu/blog/2024-01-11-m2-bert-retrieval", "href": "https://hazyresearch.stanford.edu/blog/2024-01-11-m2-bert-retrieval", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "GitHub: ", "raw": "GitHub: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/HazyResearch/m2", "href": "https://github.com/HazyResearch/m2", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Scalable Pre-training of Large Autoregressive Image Models", "raw": "Scalable Pre-training of Large Autoregressive Image Models", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "paper page: ", "raw": "paper page: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2401.08541", "href": null, "resource": { "type": "paper", "id": "2401.08541", "discussionNum": null }, "url": "https://huggingface.co/papers/2401.08541", "code": null, "user": null, "label": "Scalable Pre-training of Large Autoregressive Image Models (2401.08541)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "paper introduces AIM, a collection of vision models pre-trained with an autoregressive objective. These models are inspired by their textual counterparts, i.e., Large Language Models (LLMs), and exhibit similar scaling properties. Specifically, we highlight two key findings: (1) the performance of the visual features scale with both the model capacity and the quantity of data, (2) the value of the objective function correlates with the performance of the model on downstream tasks. We illustrate the practical implication of these findings by pre-training a 7 billion parameter AIM on 2 billion images, that achieves 84.0% on ImageNet-1k with a frozen trunk. Interestingly, even at this scale, we observe no sign of saturation in performance, suggesting that AIM potentially represents a new frontier for training large-scale vision models. The pre-training of AIM is similar to the pre-training of LLMs, and does not require any image-specific strategy to stabilize the training at scale.", "raw": "paper introduces AIM, a collection of vision models pre-trained with an autoregressive objective. These models are inspired by their textual counterparts, i.e., Large Language Models (LLMs), and exhibit similar scaling properties. Specifically, we highlight two key findings: (1) the performance of the visual features scale with both the model capacity and the quantity of data, (2) the value of the objective function correlates with the performance of the model on downstream tasks. We illustrate the practical implication of these findings by pre-training a 7 billion parameter AIM on 2 billion images, that achieves 84.0% on ImageNet-1k with a frozen trunk. Interestingly, even at this scale, we observe no sign of saturation in performance, suggesting that AIM potentially represents a new frontier for training large-scale vision models. The pre-training of AIM is similar to the pre-training of LLMs, and does not require any image-specific strategy to stabilize the training at scale.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Up to 3x faster LLM generation with no extra resources/requirements - ngram speculation has landed in 🤗 transformers! 🏎️💨", "raw": "Up to 3x faster LLM generation with no extra resources/requirements - ngram speculation has landed in 🤗 transformers! 🏎️💨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "All you need to do is to add ", "raw": "All you need to do is to add ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`prompt_lookup_num_tokens=10`", "href": null, "resource": null, "url": null, "code": "prompt_lookup_num_tokens=10", "user": null, "label": null, "lang": null }, { "type": "text", "value": " to your ", "raw": " to your ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`generate`", "href": null, "resource": null, "url": null, "code": "generate", "user": null, "label": null, "lang": null }, { "type": "text", "value": " call, and you'll get faster LLMs 🔥", "raw": " call, and you'll get faster LLMs 🔥", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "How does it work? 🤔", "raw": "How does it work? 🤔", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Start with assisted generation, where a smaller model generates candidate sequences. The net result is a significant speedup if the model agrees with the candidate sequences! However, we do require a smaller model trained similarly 😕", "raw": "Start with assisted generation, where a smaller model generates candidate sequences. The net result is a significant speedup if the model agrees with the candidate sequences! However, we do require a smaller model trained similarly 😕", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The idea introduced (and implemented) by Apoorv Saxena consists of gathering the candidate sequences from the input text itself. If the latest generated ngram is in the input, use the continuation therein as a candidate! No smaller model is required while still achieving significant speedups 🔥", "raw": "The idea introduced (and implemented) by Apoorv Saxena consists of gathering the candidate sequences from the input text itself. If the latest generated ngram is in the input, use the continuation therein as a candidate! No smaller model is required while still achieving significant speedups 🔥", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In fact, the penalty of gathering and testing the candidates is so small that you should use this technique whenever possible!", "raw": "In fact, the penalty of gathering and testing the candidates is so small that you should use this technique whenever possible!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here is the code example that produces the outputs shown in the video: ", "raw": "Here is the code example that produces the outputs shown in the video: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://pastebin.com/bms6XtR4", "href": "https://pastebin.com/bms6XtR4", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Have fun 🤗", "raw": "Have fun 🤗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Real-Time Vehicle Speed Estimation Tutorial 🚗💨💨💨", "raw": "Real-Time Vehicle Speed Estimation Tutorial 🚗💨💨💨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "TL;DR: Watch the tutorial here: ", "raw": "TL;DR: Watch the tutorial here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.youtube.com/watch?v=uWP6UjDeZvY", "href": "https://www.youtube.com/watch?v=uWP6UjDeZvY", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Key Steps:", "raw": "Key Steps:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. Vehicle Detection: Before we jump into speed estimation, we begin by detecting moving vehicles. I demonstrate this using YOLOv8, deployed through the Inference pip package.", "raw": "1. Vehicle Detection: Before we jump into speed estimation, we begin by detecting moving vehicles. I demonstrate this using YOLOv8, deployed through the Inference pip package.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. Tracking with ByteTrack: For effective object tracking, ByteTrack is my tool of choice. It assigns a unique ID to each vehicle, which is essential for accurately monitoring the distance each car travels. This forms the cornerstone of our speed calculation process.", "raw": "2. Tracking with ByteTrack: For effective object tracking, ByteTrack is my tool of choice. It assigns a unique ID to each vehicle, which is essential for accurately monitoring the distance each car travels. This forms the cornerstone of our speed calculation process.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3. Distance Calculation Complexities: Calculating traveled distance can be tricky due to perspective distortion from the camera. A car moving at a constant speed will appear to move a different number of pixels in the image, depending on its distance from the camera.", "raw": "3. Distance Calculation Complexities: Calculating traveled distance can be tricky due to perspective distortion from the camera. A car moving at a constant speed will appear to move a different number of pixels in the image, depending on its distance from the camera.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "4. Vehicle Positioning: We can accurately pinpoint each vehicle's position within our monitored area. By representing each vehicle with x and y coordinates in meters, we can compare its current and past positions, paving the way for calculating its speed.", "raw": "4. Vehicle Positioning: We can accurately pinpoint each vehicle's position within our monitored area. By representing each vehicle with x and y coordinates in meters, we can compare its current and past positions, paving the way for calculating its speed.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "5. We store the position of each car in the last second, calculate the offset, and divide it by the time delta to get the local speed.", "raw": "5. We store the position of each car in the last second, calculate the offset, and divide it by the time delta to get the local speed.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- 🔗 tutorial: ", "raw": "- 🔗 tutorial: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.youtube.com/watch?v=uWP6UjDeZvY", "href": "https://www.youtube.com/watch?v=uWP6UjDeZvY", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- 🔗 code: ", "raw": "- 🔗 code: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/roboflow/supervision/tree/develop/examples/speed_estimation", "href": "https://github.com/roboflow/supervision/tree/develop/examples/speed_estimation", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🚀 Exciting update! You can now compare multiple models side-by-side with the Hugging Face Open LLM Comparator! 📊", "raw": "🚀 Exciting update! You can now compare multiple models side-by-side with the Hugging Face Open LLM Comparator! 📊", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/open-llm-leaderboard/comparator", "href": null, "resource": { "type": "space", "id": "open-llm-leaderboard/comparator", "discussionNum": null }, "url": "https://huggingface.co/spaces/open-llm-leaderboard/comparator", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Dive into multi-model evaluations, pinpoint the best model for your needs, and explore insights across top open LLMs all in one place. Ready to level up your model comparison game?", "raw": "Dive into multi-model evaluations, pinpoint the best model for your needs, and explore insights across top open LLMs all in one place. Ready to level up your model comparison game?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "New Dataset: Software-Architecture", "raw": "New Dataset: Software-Architecture", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Link: ", "raw": "Link: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/ajibawa-2023/Software-Architecture", "href": null, "resource": { "type": "dataset", "id": "ajibawa-2023/Software-Architecture", "discussionNum": null }, "url": "https://huggingface.co/datasets/ajibawa-2023/Software-Architecture", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I am releasing a Large Dataset covering topics related to Software-Architecture. This dataset consists of around 450,000 lines of data in jsonl.", "raw": "I am releasing a Large Dataset covering topics related to Software-Architecture. This dataset consists of around 450,000 lines of data in jsonl.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I have included following topics:", "raw": "I have included following topics:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { 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Also those who are working on developing Software development related LLMs then this dataset can be useful.", "raw": "This dataset is useful in LLM development. Also those who are working on developing Software development related LLMs then this dataset can be useful.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This dataset is very useful to Researchers as well.", "raw": "This dataset is very useful to Researchers as well.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Check out The AI Writing Contest with ", "raw": "Check out The AI Writing Contest with ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@BrightData", "href": null, "resource": null, "url": null, "code": null, "user": "BrightData", "label": null, "lang": null }, { "type": "text", "value": "! ", "raw": "! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.contests.hackernoon.com/ai-writing-contest", "href": "https://www.contests.hackernoon.com/ai-writing-contest", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " Cash prizes for innovative approaches to AI and LLM training. We publish blog posts, research papers, stories of side hustles, you name it.", "raw": " Cash prizes for innovative approaches to AI and LLM training. We publish blog posts, research papers, stories of side hustles, you name it.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Any story tagged #AI enters to win. Most recent stories: ", "raw": "Any story tagged #AI enters to win. 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[ { "type": "text", "value": "I'm very proud to have supported ", "raw": "I'm very proud to have supported ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@CGIAR", "href": null, "resource": null, "url": null, "code": null, "user": "CGIAR", "label": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Digigreen", "href": null, "resource": null, "url": null, "code": null, "user": "Digigreen", "label": null, "lang": null }, { "type": "text", "value": " in making ", "raw": " in making ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "http://Farmer.chat", "href": "http://Farmer.chat", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ", an app that supports 20k smallholder farmers on a daily basis 🌾", "raw": ", an app that supports 20k smallholder farmers on a daily basis 🌾", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "There are ~500 million smallholder farmers globally, playing a critical role in global food security. Having access to accurate information is essential for them.", "raw": "There are ~500 million smallholder farmers globally, playing a critical role in global food security. Having access to accurate information is essential for them.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💬 An “agricultural extension service” offers technical advice on agriculture, and also supplies farmers with the necessary inputs and services to support their agricultural production.", "raw": "💬 An “agricultural extension service” offers technical advice on agriculture, and also supplies farmers with the necessary inputs and services to support their agricultural production.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "But agriculture extension agents are not in large enough numbers to cope with all the requests, especially in countries like Kenya, India, Ethiopia, and Nigeria.", "raw": "But agriculture extension agents are not in large enough numbers to cope with all the requests, especially in countries like Kenya, India, Ethiopia, and Nigeria.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🚀 So the team set out to build an app called ", "raw": "🚀 So the team set out to build an app called ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "http://Farmer.Chat", "href": "http://Farmer.Chat", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ", to provide an agricultural extension service, by building on the immense knowledge accumulated by CGIAR.", "raw": ", to provide an agricultural extension service, by building on the immense knowledge accumulated by CGIAR.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "✨ The app is technically impressive: behind the Whatsapp-type UX, an agent interprets the user's intent, and identifies which tool to call to best answer their request: weather API, RAG on a CGIAR-provided knowledge base, market data, etc. The RAG on the knowledge base is in itself a work of art.", "raw": "✨ The app is technically impressive: behind the Whatsapp-type UX, an agent interprets the user's intent, and identifies which tool to call to best answer their request: weather API, RAG on a CGIAR-provided knowledge base, market data, etc. The RAG on the knowledge base is in itself a work of art.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🎯 A key part of building such a complex system is to be able to evaluate it properly. During our bi-weekly sessions with the team, I could support them in implementing the method called \"LLM-as-a-judge\" to tackle this problem.", "raw": "🎯 A key part of building such a complex system is to be able to evaluate it properly. During our bi-weekly sessions with the team, I could support them in implementing the method called \"LLM-as-a-judge\" to tackle this problem.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It worked really well : thanks to the amazing work of the team, the app now successfully answered over 300 thousand requests, in 6 different languages, and it keeps growing!", "raw": "It worked really well : thanks to the amazing work of the team, the app now successfully answered over 300 thousand requests, in 6 different languages, and it keeps growing!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "➡️ ", "raw": "➡️ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Vinsingh", "href": null, "resource": null, "url": null, "code": null, "user": "Vinsingh", "label": null, "lang": null }, { "type": "text", "value": ", ", "raw": ", ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@rajgreen", "href": null, "resource": null, "url": null, "code": null, "user": "rajgreen", "label": null, "lang": null }, { "type": "text", "value": " and I just wrote a blog post to describe how the app works, especially the LLM-as-a-judge system!", "raw": " and I just wrote a blog post to describe how the app works, especially the LLM-as-a-judge system!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Read it here 👉 ", "raw": "Read it here 👉 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/digital-green-llm-judge", "href": "https://huggingface.co/blog/digital-green-llm-judge", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "If you are like me, I like to find up and coming datasets and spaces before everyone else.", "raw": "If you are like me, I like to find up and coming datasets and spaces before everyone else.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I made a trending repo space ", "raw": "I made a trending repo space ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/cfahlgren1/trending-repos", "href": null, "resource": { "type": "space", "id": "cfahlgren1/trending-repos", "discussionNum": null }, "url": "https://huggingface.co/spaces/cfahlgren1/trending-repos", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " where it shows:", "raw": " where it shows:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- New up and coming Spaces in the last day", "raw": "- New up and coming Spaces in the last day", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- New up and coming Datasets in the last 2 weeks", "raw": "- New up and coming Datasets in the last 2 weeks", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It's a really good way to find some new gems before they become popular. For example, someone is working on a way to dynamically create assets inside a video game here: ", "raw": "It's a really good way to find some new gems before they become popular. For example, someone is working on a way to dynamically create assets inside a video game here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/gptcall/AI-Game-Creator", "href": null, "resource": { "type": "space", "id": "gptcall/AI-Game-Creator", "discussionNum": null }, "url": "https://huggingface.co/spaces/gptcall/AI-Game-Creator", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Hello, researchers! I've tried to made reading HF Daily Papers easier and made a tool that does reviews with LLMs like Claude 3.5, GPT-4o and sometimes FLUX.", "raw": "Hello, researchers! I've tried to made reading HF Daily Papers easier and made a tool that does reviews with LLMs like Claude 3.5, GPT-4o and sometimes FLUX.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📚 Classification by topics", "raw": "📚 Classification by topics", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📅 Sorting by publication date and HF addition date", "raw": "📅 Sorting by publication date and HF addition date", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🔄 Syncing every 2 hours", "raw": "🔄 Syncing every 2 hours", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💻 Hosted on GitHub ", "raw": "💻 Hosted on GitHub ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🌏 English, Russian, and Chinese", "raw": "🌏 English, Russian, and Chinese", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📈 Top by week/month (in progress)", "raw": "📈 Top by week/month (in progress)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "👉 ", "raw": "👉 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://hfday.ru", "href": "https://hfday.ru", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Let me know what do you think of it.", "raw": "Let me know what do you think of it.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Hugging Face Hub Python library now comes with easy inference for vision language models! ✨", "raw": "Hugging Face Hub Python library now comes with easy inference for vision language models! ✨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "$ pip install huggingface_hub 🤗", "raw": "$ pip install huggingface_hub 🤗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Good folks from ", "raw": "Good folks from ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Microsoft", "href": null, "resource": null, "url": null, "code": null, "user": "Microsoft", "label": null, "lang": null }, { "type": "text", "value": " have released an exciting breakthrough in GUI automation!", "raw": " have released an exciting breakthrough in GUI automation!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "OmniParser – a game-changing approach for pure vision-based GUI agents that works across multiple platforms and applications.", "raw": "OmniParser – a game-changing approach for pure vision-based GUI agents that works across multiple platforms and applications.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Key technical innovations:", "raw": "Key technical innovations:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Custom-trained interactable icon detection model using 67k screenshots from popular websites", "raw": "- Custom-trained interactable icon detection model using 67k screenshots from popular websites", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Specialized BLIP-v2 model fine-tuned on 7k icon-description pairs for extracting functional semantics", "raw": "- Specialized BLIP-v2 model fine-tuned on 7k icon-description pairs for extracting functional semantics", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Novel combination of icon detection, OCR, and semantic understanding to create structured UI representations", "raw": "- Novel combination of icon detection, OCR, and semantic understanding to create structured UI representations", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The results are impressive:", "raw": "The results are impressive:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Outperforms GPT-4V baseline by significant margins on the ScreenSpot benchmark", "raw": "- Outperforms GPT-4V baseline by significant margins on the ScreenSpot benchmark", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Achieves 73% accuracy on Mind2Web without requiring HTML data", "raw": "- Achieves 73% accuracy on Mind2Web without requiring HTML data", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Demonstrates a 57.7% success rate on AITW mobile tasks", "raw": "- Demonstrates a 57.7% success rate on AITW mobile tasks", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What makes OmniParser special is its ability to work across platforms (mobile, desktop, web) using only screenshot data – no HTML or view hierarchy needed. This opens up exciting possibilities for building truly universal GUI automation tools.", "raw": "What makes OmniParser special is its ability to work across platforms (mobile, desktop, web) using only screenshot data – no HTML or view hierarchy needed. This opens up exciting possibilities for building truly universal GUI automation tools.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The team has open-sourced both the interactable region detection dataset and icon description dataset to accelerate research in this space.", "raw": "The team has open-sourced both the interactable region detection dataset and icon description dataset to accelerate research in this space.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Kudos to the Microsoft Research team for pushing the boundaries of what's possible with pure vision-based GUI understanding!", "raw": "Kudos to the Microsoft Research team for pushing the boundaries of what's possible with pure vision-based GUI understanding!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What are your thoughts on vision-based GUI automation?", "raw": "What are your thoughts on vision-based GUI automation?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Allegro: New Open Source SOTA Text to Image Model - 27 Amazing Examples With Prompts, Apache 2.0 License - Models and inference code published already", "raw": "Allegro: New Open Source SOTA Text to Image Model - 27 Amazing Examples With Prompts, Apache 2.0 License - Models and inference code published already", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Video to watch all : ", "raw": "Video to watch all : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.youtube.com/watch?v=0tsLqNXQ5Mk", "href": "https://www.youtube.com/watch?v=0tsLqNXQ5Mk", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Official repo : ", "raw": "Official repo : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/rhymes-ai/Allegro", "href": "https://github.com/rhymes-ai/Allegro", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Hugging Face : ", "raw": "Hugging Face : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/rhymes-ai/Allegro", "href": null, "resource": { "type": "model", "id": "rhymes-ai/Allegro", "discussionNum": null }, "url": "https://huggingface.co/rhymes-ai/Allegro", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper : ", "raw": "Paper : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2410.15458", "href": "https://arxiv.org/abs/2410.15458", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Introducing Lemone-router, a series of classification models designed to produce an optimal multi-agent system for different branches of tax law.", "raw": "Introducing Lemone-router, a series of classification models designed to produce an optimal multi-agent system for different branches of tax law.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Trained on a base of 49k lines comprising a set of synthetic questions generated by GPT-4 Turbo and Llama 3.1 70B, which have been further refined through evol-instruction tuning and manual curation and authority documents, these models are based on an 8-category decomposition of the classification scheme derived from the Bulletin officiel des finances publiques - impôts :", "raw": "Trained on a base of 49k lines comprising a set of synthetic questions generated by GPT-4 Turbo and Llama 3.1 70B, which have been further refined through evol-instruction tuning and manual curation and authority documents, these models are based on an 8-category decomposition of the classification scheme derived from the Bulletin officiel des finances publiques - impôts :", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```python\nlabel2id = {\n \"Bénéfices professionnels\": 0,\n \"Contrôle et contentieux\": 1,\n \"Dispositifs transversaux\": 2,\n \"Fiscalité des entreprises\": 3,\n \"Patrimoine et enregistrement\": 4,\n \"Revenus particuliers\": 5,\n \"Revenus patrimoniaux\": 6,\n \"Taxes sur la consommation\": 7\n}\n\t\nid2label = {\n 0: \"Bénéfices professionnels\",\n 1: \"Contrôle et contentieux\",\n 2: \"Dispositifs transversaux\",\n 3: \"Fiscalité des entreprises\",\n 4: \"Patrimoine et enregistrement\",\n 5: \"Revenus particuliers\",\n 6: \"Revenus patrimoniaux\",\n 7: \"Taxes sur la consommation\"\n}\n```", "href": null, "resource": null, "url": null, "code": "label2id = {\n \"Bénéfices professionnels\": 0,\n \"Contrôle et contentieux\": 1,\n \"Dispositifs transversaux\": 2,\n \"Fiscalité des entreprises\": 3,\n \"Patrimoine et enregistrement\": 4,\n \"Revenus particuliers\": 5,\n \"Revenus patrimoniaux\": 6,\n \"Taxes sur la consommation\": 7\n}\n\t\nid2label = {\n 0: \"Bénéfices professionnels\",\n 1: \"Contrôle et contentieux\",\n 2: \"Dispositifs transversaux\",\n 3: \"Fiscalité des entreprises\",\n 4: \"Patrimoine et enregistrement\",\n 5: \"Revenus particuliers\",\n 6: \"Revenus patrimoniaux\",\n 7: \"Taxes sur la consommation\"\n}", "user": null, "label": null, "lang": "python" }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It achieves the following results on the evaluation set:", "raw": "It achieves the following results on the evaluation set:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Loss: 0.4734", "raw": "- Loss: 0.4734", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Accuracy: 0.9191", "raw": "- Accuracy: 0.9191", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Link to the collection: ", "raw": "Link to the collection: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/louisbrulenaudet/lemone-router-671cce21d6410f3570514762", "href": null, "resource": { "type": "collection", "id": "louisbrulenaudet/lemone-router-671cce21d6410f3570514762", "discussionNum": null }, "url": "https://huggingface.co/collections/louisbrulenaudet/lemone-router-671cce21d6410f3570514762", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "💥 Today's pick in Interpretability & Analysis of LMs: Fine-grained Hallucination Detection and Editing For Language Models by ", "raw": "💥 Today's pick in Interpretability & Analysis of LMs: Fine-grained Hallucination Detection and Editing For Language Models by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@abhika-m", "href": null, "resource": null, "url": null, "code": null, "user": "abhika-m", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@akariasai", "href": null, "resource": null, "url": null, "code": null, "user": "akariasai", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, 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and editing tasks.", "raw": "Authors introduce a new taxonomy for fine-grained annotation of hallucinations in LM generations and propose Factuality Verification with Augmented Knowledge (FAVA), a retrieval-augmented LM fine-tuned to detect and edit hallucinations in LM outputs, outperforming ChatGPT and LLama2 Chat on both detection and editing tasks.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🌐 Website: ", "raw": "🌐 Website: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": 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null, "label": null, "lang": null }, { "type": "text", "value": "🚀 Demo: ", "raw": "🚀 Demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/fava-uw/fava", "href": null, "resource": { "type": "space", "id": "fava-uw/fava", "discussionNum": null }, "url": "https://huggingface.co/spaces/fava-uw/fava", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🤖 Model: ", "raw": "🤖 Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/fava-uw/fava-model", "href": null, "resource": { "type": "model", "id": "fava-uw/fava-model", "discussionNum": null }, "url": "https://huggingface.co/fava-uw/fava-model", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🔡 Dataset: ", "raw": "🔡 Dataset: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/fava-uw/fava-data", "href": null, "resource": { "type": "dataset", "id": "fava-uw/fava-data", "discussionNum": null }, "url": "https://huggingface.co/datasets/fava-uw/fava-data", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "GPU Poor POV: Building a RAG which solves specific task.", "raw": "GPU Poor POV: Building a RAG which solves specific task.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Everyone loves benchmarks.", "raw": "Everyone loves benchmarks.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "They are great because we have standarized approach, competitive feeling. But if you are in specific area, trying to implement some LLM/RAG use case, these benchmarks cannot exactly reflect on the data that you have to deal with.", "raw": "They are great because we have standarized approach, competitive feeling. But if you are in specific area, trying to implement some LLM/RAG use case, these benchmarks cannot exactly reflect on the data that you have to deal with.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I built RAG system on bunch of niche procedures/regulation etc, which can be finally deployed as an virtual assistant to minimize the effort in searching through a lot of documentations manually. ", "raw": "I built RAG system on bunch of niche procedures/regulation etc, which can be finally deployed as an virtual assistant to minimize the effort in searching through a lot of documentations manually. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Tested a lot of different methods/models/pretrains, finetunes and whats interesting is that, final solution which was scored by human feedback is based on relatively low param models, with multitask ability ", "raw": "Tested a lot of different methods/models/pretrains, finetunes and whats interesting is that, final solution which was scored by human feedback is based on relatively low param models, with multitask ability ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Something like:", "raw": "Something like:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/BAAI/llm-embedder", "href": null, "resource": { "type": "model", "id": "BAAI/llm-embedder", "discussionNum": null }, "url": "https://huggingface.co/BAAI/llm-embedder", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "LLMs help summarize the chunk version of knowledge base found, does not require the model with high number of params, because tradeoff between inference time and accuracy has to be made. Some lightweight models have ability to perform certain task based on instructions, so eg. qwen 7b or mistral 7b (not moe one), realized a task really nicely. And what is more important is that in overall we are able to deploy a RAG system in smaller tasks, in specific area. They can be used by people who need it, give additive value and positive feedback, which IMO is what is all of the building process about. ", "raw": "LLMs help summarize the chunk version of knowledge base found, does not require the model with high number of params, because tradeoff between inference time and accuracy has to be made. Some lightweight models have ability to perform certain task based on instructions, so eg. qwen 7b or mistral 7b (not moe one), realized a task really nicely. And what is more important is that in overall we are able to deploy a RAG system in smaller tasks, in specific area. They can be used by people who need it, give additive value and positive feedback, which IMO is what is all of the building process about. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Have a great day and think about problem which your models have to solve <3", "raw": "Have a great day and think about problem which your models have to solve <3", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "What is the current SOTA in terms of fast personalized image generation? Most of the techniques that produce great results (which is hard to objectively measure, but subject similarity index being close to 80-90%) take either too much time (full on DreamBooth fine-tuning the base model) or or loose on the auxilary properties (high rank LoRAs).", "raw": "What is the current SOTA in terms of fast personalized image generation? Most of the techniques that produce great results (which is hard to objectively measure, but subject similarity index being close to 80-90%) take either too much time (full on DreamBooth fine-tuning the base model) or or loose on the auxilary properties (high rank LoRAs).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We have been also testing face embeddings, but even with multiple samples the quality is not anywhere close to what we expect. Even on the techniques that work, high quality (studio-level) pictures seem to be a must so another avenue that I'm curious is whether there is filter/segmentation of the input samples in an automatic way that people have looked in the past?", "raw": "We have been also testing face embeddings, but even with multiple samples the quality is not anywhere close to what we expect. Even on the techniques that work, high quality (studio-level) pictures seem to be a must so another avenue that I'm curious is whether there is filter/segmentation of the input samples in an automatic way that people have looked in the past?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Google's SigLIP is another alternative to openai's CLIP, and it just got merged to 🤗transformers and it's super easy to use!", "raw": "Google's SigLIP is another alternative to openai's CLIP, and it just got merged to 🤗transformers and it's super easy to use!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "To celebrate this, I have created a repository including notebooks and bunch of Spaces on various SigLIP based projects 🥳 ", "raw": "To celebrate this, I have created a repository including notebooks and bunch of Spaces on various SigLIP based projects 🥳 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Search for art 👉 ", "raw": "Search for art 👉 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/merve/draw_to_search_art", "href": null, "resource": { "type": "space", "id": "merve/draw_to_search_art", "discussionNum": null }, "url": "https://huggingface.co/spaces/merve/draw_to_search_art", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Compare SigLIP with CLIP 👉 ", "raw": "Compare SigLIP with CLIP 👉 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/merve/compare_clip_siglip", "href": null, "resource": { "type": "space", "id": "merve/compare_clip_siglip", "discussionNum": null }, "url": "https://huggingface.co/spaces/merve/compare_clip_siglip", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "How does SigLIP work? ", "raw": "How does SigLIP work? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "SigLIP an vision-text pre-training technique based on contrastive learning. It jointly trains an image encoder and text encoder such that the dot product of embeddings are most similar for the appropriate text-image pairs", "raw": "SigLIP an vision-text pre-training technique based on contrastive learning. It jointly trains an image encoder and text encoder such that the dot product of embeddings are most similar for the appropriate text-image pairs", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The image below is taken from CLIP, where this contrastive pre-training takes place with softmax, but SigLIP replaces softmax with sigmoid. 📎 ", "raw": "The image below is taken from CLIP, where this contrastive pre-training takes place with softmax, but SigLIP replaces softmax with sigmoid. 📎 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Highlights from the paper on why you should use it ✨", "raw": "Highlights from the paper on why you should use it ✨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🖼️📝 Authors used medium sized B/16 ViT for image encoder and B-sized transformer for text encoder", "raw": "🖼️📝 Authors used medium sized B/16 ViT for image encoder and B-sized transformer for text encoder", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "😍 More performant than CLIP on zero-shot", "raw": "😍 More performant than CLIP on zero-shot", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🗣️ Authors trained a multilingual model too!", "raw": "🗣️ Authors trained a multilingual model too!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "⚡️ Super efficient, sigmoid is enabling up to 1M items per batch, but the authors chose 32k because the performance saturates after that", "raw": "⚡️ Super efficient, sigmoid is enabling up to 1M items per batch, but the authors chose 32k because the performance saturates after that", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It's super easy to use thanks to transformers 👇 ", "raw": "It's super easy to use thanks to transformers 👇 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```python\nfrom transformers import pipeline\nfrom PIL import Image\nimport requests\n\n# load pipe\nimage_classifier = pipeline(task=\"zero-shot-image-classification\", model=\"google/siglip-base-patch16-256-i18n\")\n\n# load image\nurl = 'http://images.cocodataset.org/val2017/000000039769.jpg'\nimage = Image.open(requests.get(url, stream=True).raw)\n\n# inference\noutputs = image_classifier(image, candidate_labels=[\"2 cats\", \"a plane\", \"a remote\"])\noutputs = [{\"score\": round(output[\"score\"], 4), \"label\": output[\"label\"] } for output in outputs]\nprint(outputs)\n```", "href": null, "resource": null, "url": null, "code": "from transformers import pipeline\nfrom PIL import Image\nimport requests\n\n# load pipe\nimage_classifier = pipeline(task=\"zero-shot-image-classification\", model=\"google/siglip-base-patch16-256-i18n\")\n\n# load image\nurl = 'http://images.cocodataset.org/val2017/000000039769.jpg'\nimage = Image.open(requests.get(url, stream=True).raw)\n\n# inference\noutputs = image_classifier(image, candidate_labels=[\"2 cats\", \"a plane\", \"a remote\"])\noutputs = [{\"score\": round(output[\"score\"], 4), \"label\": output[\"label\"] } for output in outputs]\nprint(outputs)", "user": null, "label": null, "lang": "python" }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For all the SigLIP notebooks on similarity search and indexing, you can check this [repository](", "raw": "For all the SigLIP notebooks on similarity search and indexing, you can check this [repository](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/merveenoyan/siglip", "href": "https://github.com/merveenoyan/siglip", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") out. 🤗 ", "raw": ") out. 🤗 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Is synthetic data the future of AI? 🔥🔥🔥", "raw": "Is synthetic data the future of AI? 🔥🔥🔥", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@HugoLaurencon", "href": null, "resource": null, "url": null, "code": null, "user": "HugoLaurencon", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Leyo", "href": null, "resource": null, "url": null, "code": null, "user": "Leyo", "label": null, "lang": null }, { "type": "text", "value": " & ", "raw": " & ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@VictorSanh", "href": null, "resource": null, "url": null, "code": null, "user": "VictorSanh", "label": null, "lang": null }, { "type": "text", "value": " are introducing ", "raw": " are introducing ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/HuggingFaceM4/WebSight", "href": null, "resource": { "type": "dataset", "id": "HuggingFaceM4/WebSight", "discussionNum": null }, "url": "https://huggingface.co/datasets/HuggingFaceM4/WebSight", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " , a multimodal dataset featuring 823,000 pairs of synthetically generated HTML/CSS codes along with screenshots of the corresponding rendered websites to train GPT4-V-like models 🌐💻", "raw": " , a multimodal dataset featuring 823,000 pairs of synthetically generated HTML/CSS codes along with screenshots of the corresponding rendered websites to train GPT4-V-like models 🌐💻", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "While crafting their upcoming foundation vision language model, they faced the challenge of converting website screenshots into usable HTML/CSS codes. Most VLMs suck at this and there was no public dataset available for this specific task, so they decided to create their own.", "raw": "While crafting their upcoming foundation vision language model, they faced the challenge of converting website screenshots into usable HTML/CSS codes. Most VLMs suck at this and there was no public dataset available for this specific task, so they decided to create their own.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "They prompted existing LLMs to generate 823k HTML/CSS codes of very simple websites. Through supervised fine-tuning of a vision language model on WebSight, they were able to generate the code to reproduce a website component, given a screenshot.", "raw": "They prompted existing LLMs to generate 823k HTML/CSS codes of very simple websites. Through supervised fine-tuning of a vision language model on WebSight, they were able to generate the code to reproduce a website component, given a screenshot.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can explore the dataset here: ", "raw": "You can explore the dataset here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/HuggingFaceM4/WebSight", "href": null, "resource": { "type": "dataset", "id": "HuggingFaceM4/WebSight", "discussionNum": null }, "url": "https://huggingface.co/datasets/HuggingFaceM4/WebSight", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What do you think?", "raw": "What do you think?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Happy to announce, brand new, open-source Hugging Face Competitions platform 🚀 Now, create a machine learning competition for your friends, colleagues or the world for FREE* and host it on Hugging Face: the AI community building the future. Creating a competition requires only two steps: ", "raw": "Happy to announce, brand new, open-source Hugging Face Competitions platform 🚀 Now, create a machine learning competition for your friends, colleagues or the world for FREE* and host it on Hugging Face: the AI community building the future. Creating a competition requires only two steps: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`pip install competitions`", "href": null, "resource": null, "url": null, "code": "pip install competitions", "user": null, "label": null, "lang": null }, { "type": "text", "value": ", then run ", "raw": ", then run ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`competitions create`", "href": null, "resource": null, "url": null, "code": "competitions create", "user": null, "label": null, "lang": null }, { "type": "text", "value": " and create competition by answering a few questions 💥 Checkout the github repo: ", "raw": " and create competition by answering a few questions 💥 Checkout the github repo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/huggingface/competitions", "href": "https://github.com/huggingface/competitions", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " and docs: ", "raw": " and docs: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://hf.co/docs/competitions", "href": "https://hf.co/docs/competitions", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🙋🏻‍♂️hey there folks,", "raw": "🙋🏻‍♂️hey there folks,", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Everyone's🗣️talking about microsoft's new e5mistral embeddings model", "raw": "Everyone's🗣️talking about microsoft's new e5mistral embeddings model", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🤔🤔 but did you actually try it yet ? ", "raw": "🤔🤔 but did you actually try it yet ? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Well , now you can, just check it out. it's a new way to serve and create embeddings. ", "raw": "Well , now you can, just check it out. it's a new way to serve and create embeddings. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "try it hosted on GPUZero : ", "raw": "try it hosted on GPUZero : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Tonic/e5", "href": null, "resource": { "type": "space", "id": "Tonic/e5", "discussionNum": null }, "url": "https://huggingface.co/spaces/Tonic/e5", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "or served on an A10G : ", "raw": "or served on an A10G : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Tonic/e5", "href": null, "resource": { "type": "space", "id": "Tonic/e5", "discussionNum": null }, "url": "https://huggingface.co/spaces/Tonic/e5", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "you get best results actually building with it though, so use it in your app !", "raw": "you get best results actually building with it though, so use it in your app !", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Our demo is coming soon too so let's work together if you want :-)", "raw": "Our demo is coming soon too so let's work together if you want :-)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Just published a quick community blog post mainly aimed at Art and Design students, but which is also an attempt to nudge AI researchers who would like to better consider benefits from collaboration with designers and artists 😉 ", "raw": "Just published a quick community blog post mainly aimed at Art and Design students, but which is also an attempt to nudge AI researchers who would like to better consider benefits from collaboration with designers and artists 😉 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Feel free to share your thoughts !", "raw": "Feel free to share your thoughts !", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "\"Breaking Barriers: The Critical Role of Art and Design in Advancing AI Capabilities\" 📄 ", "raw": "\"Breaking Barriers: The Critical Role of Art and Design in Advancing AI Capabilities\" 📄 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/fffiloni/the-critical-role-of-art-and-design-in-advancing-a", "href": "https://huggingface.co/blog/fffiloni/the-critical-role-of-art-and-design-in-advancing-a", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "—", "raw": "—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This short publication follows the results of two AI Workshops that took place at École des Arts Décoratifs - Paris, lead by Etienne Mineur, Vadim Bernard, Martin de Bie, Antoine Pintout & Sylvain Filoni.", "raw": "This short publication follows the results of two AI Workshops that took place at École des Arts Décoratifs - Paris, lead by Etienne Mineur, Vadim Bernard, Martin de Bie, Antoine Pintout & Sylvain Filoni.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "💡We recently launched a Discord server for #Dutch #NLP and #LLMs. We have more than 50 users of _very_ varrying backgrounds! 🧙👩‍🔬🧑‍🎨🧑‍🏫🧑‍💼 We've already had discussions on eval, tokenizers, RAG, data... A bit of everything. Everyone is welcome to work together on Dutch NLP and LLMs! ", "raw": "💡We recently launched a Discord server for #Dutch #NLP and #LLMs. We have more than 50 users of _very_ varrying backgrounds! 🧙👩‍🔬🧑‍🎨🧑‍🏫🧑‍💼 We've already had discussions on eval, tokenizers, RAG, data... A bit of everything. Everyone is welcome to work together on Dutch NLP and LLMs! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://discord.gg/YUUXVZkZJ9", "href": "https://discord.gg/YUUXVZkZJ9", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "As the amount of datasets for fine tuning chat models has grown, there's been a plethora of dataset formats emerge. The most popular of these include the formats used by Alpaca, ShareGPT and Open Assistant datasets. The datasets and their formats have also evolved from single-turn conversation to multi-turn. Many of these formats share similarities (and they all have the same goal), but handling the variations in formats across datasets is often a hassle, and source of potential bugs.", "raw": "As the amount of datasets for fine tuning chat models has grown, there's been a plethora of dataset formats emerge. The most popular of these include the formats used by Alpaca, ShareGPT and Open Assistant datasets. The datasets and their formats have also evolved from single-turn conversation to multi-turn. Many of these formats share similarities (and they all have the same goal), but handling the variations in formats across datasets is often a hassle, and source of potential bugs.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Luckily the community seems to be converging on a simple and elegant chat dataset format: a list with each record being an array with each conversation turn being an object with a role (system, assistant or user) and content. Hugging Face uses this input format in the [Templates for Chat Models](", "raw": "Luckily the community seems to be converging on a simple and elegant chat dataset format: a list with each record being an array with each conversation turn being an object with a role (system, assistant or user) and content. Hugging Face uses this input format in the [Templates for Chat Models](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/docs/transformers/main/en/chat_templating#how-do-i-use-chat-templates", "href": "https://huggingface.co/docs/transformers/main/en/chat_templating#how-do-i-use-chat-templates", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") docs:", "raw": ") docs:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```python\nmessages = [\n {\n \"role\": \"system\",\n \"content\": \"You are a friendly chatbot who always responds in the style of a pirate\",\n },\n {\"role\": \"user\", \"content\": \"How many helicopters can a human eat in one sitting?\"},\n]\n```", "href": null, "resource": null, "url": null, "code": "messages = [\n {\n \"role\": \"system\",\n \"content\": \"You are a friendly chatbot who always responds in the style of a pirate\",\n },\n {\"role\": \"user\", \"content\": \"How many helicopters can a human eat in one sitting?\"},\n]", "user": null, "label": null, "lang": "python" }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Popular datasets like ", "raw": "Popular datasets like ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/HuggingFaceH4/no_robots", "href": null, "resource": { "type": "dataset", "id": "HuggingFaceH4/no_robots", "discussionNum": null }, "url": "https://huggingface.co/datasets/HuggingFaceH4/no_robots", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " follow this format.", "raw": " follow this format.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "To encourage usage of this format, I propose we give it a name: Hugging Face MessagesList format.", "raw": "To encourage usage of this format, I propose we give it a name: Hugging Face MessagesList format.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The format is defined as:", "raw": "The format is defined as:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Having at least one ", "raw": "- Having at least one ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`messages`", "href": null, "resource": null, "url": null, "code": "messages", "user": null, "label": null, "lang": null }, { "type": "text", "value": " column of type list.", "raw": " column of type list.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Each messages record is an array containing one or more message turn objects.", "raw": "- Each messages record is an array containing one or more message turn objects.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- A message turn must have ", "raw": "- A message turn must have ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`role`", "href": null, "resource": null, "url": null, "code": "role", "user": null, "label": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`content`", "href": null, "resource": null, "url": null, "code": "content", "user": null, "label": null, "lang": null }, { "type": "text", "value": " keys.", "raw": " keys.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- ", "raw": "- ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`role`", "href": null, "resource": null, "url": null, "code": "role", "user": null, "label": null, "lang": null }, { "type": "text", "value": " should be one of ", "raw": " should be one of ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`system`", "href": null, "resource": null, "url": null, "code": "system", "user": null, "label": null, "lang": null }, { "type": "text", "value": ", ", "raw": ", ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`assistant`", "href": null, "resource": null, "url": null, "code": "assistant", "user": null, "label": null, "lang": null }, { "type": "text", "value": " or ", "raw": " or ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`user`", "href": null, "resource": null, "url": null, "code": "user", "user": null, "label": null, "lang": null }, { "type": "text", "value": ".", "raw": ".", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- ", "raw": "- ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`content`", "href": null, "resource": null, "url": null, "code": "content", "user": null, "label": null, "lang": null }, { "type": "text", "value": " is the text content of the message.", "raw": " is the text content of the message.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This may be a small thing, but having a common dataset format will reduce wasted time data wrangling and help everyone.", "raw": "This may be a small thing, but having a common dataset format will reduce wasted time data wrangling and help everyone.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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