Datasets:

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maxiw

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hf-posts

like 18

[ { "type": "text", "value": "𝟐𝟎𝟐𝟒, 𝐭𝐡𝐞 𝐲𝐞𝐚𝐫 𝐨𝐟 𝐚𝐠𝐞𝐧𝐭 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 🔧🦾🤖", "raw": "𝟐𝟎𝟐𝟒, 𝐭𝐡𝐞 𝐲𝐞𝐚𝐫 𝐨𝐟 𝐚𝐠𝐞𝐧𝐭 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 🔧🦾🤖", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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've just watched Andrew Ng's talk at Sequoia last week.", "raw": "I've just watched Andrew Ng's talk at Sequoia last 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": "text", "value": "If you're interested in Agents, you should really watch it!", "raw": "If you're interested in Agents, you should really watch 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": "𝗪𝗵𝘆 𝘂𝘀𝗲 𝗮𝗴𝗲𝗻𝘁 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀?", "raw": "𝗪𝗵𝘆 𝘂𝘀𝗲 𝗮𝗴𝗲𝗻𝘁 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 current LLM task solving workflow is not very intuitive:", "raw": "The current LLM task solving workflow is not very intuitive:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 ask it “write an essay all in one shot, without ever using backspace.”", "raw": "We ask it “write an essay all in one shot, without ever using backspace.”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 not allow the LLM a more similar process to what we would do?", "raw": "Why not allow the LLM a more similar process to what we would 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": "text", "value": "- “Write an essay outline”", "raw": "- “Write an essay outline”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 you need wen research?”", "raw": "- “Do you need wen research?”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- “Write a first draft”", "raw": "- “Write a first draft”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- “Consider improvements”", "raw": "- “Consider improvements”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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": "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 called an Agentic workflow. Existing ones bring a huge performance boost. With HumanEval: GPT-4 zero-shot gets 67% score, agentic with either one of tool use or reflection goes over 90%, and the combination of the two scores even higher!", "raw": "This is called an Agentic workflow. Existing ones bring a huge performance boost. With HumanEval: GPT-4 zero-shot gets 67% score, agentic with either one of tool use or reflection goes over 90%, and the combination of the two scores even higher!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗱𝗲𝘀𝗶𝗴𝗻 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀", "raw": "𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗱𝗲𝘀𝗶𝗴𝗻 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "On the following two points, the tech is robust:", "raw": "On the following two points, the tech is 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": "⚙️ 𝗥𝗲𝗳𝗹𝗲𝘅𝗶𝗼𝗻: For instance: add a critic step after the writing step", "raw": "⚙️ 𝗥𝗲𝗳𝗹𝗲𝘅𝗶𝗼𝗻: For instance: add a critic step after the writing step", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🛠️ 𝗧𝗼𝗼𝗹 𝘂𝘀𝗲: extends the capabilities of the LLM by allowing it to call tools, like search or calculator", "raw": "🛠️ 𝗧𝗼𝗼𝗹 𝘂𝘀𝗲: extends the capabilities of the LLM by allowing it to call tools, like search or calculator", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 next two will be needed to go further, but the tech for them is more emerging and not reliable yet:", "raw": "The next two will be needed to go further, but the tech for them is more emerging and not reliable 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": "text", "value": "🗺️ 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 forward to decompose task into subtasks. This allows great behaviours like an AI Agent re-routing after a failure", "raw": "🗺️ 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 forward to decompose task into subtasks. This allows great behaviours like an AI Agent re-routing after 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": "text", "value": "🐝 𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Program a flock of agents with tasks.", "raw": "🐝 𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Program a flock of agents with 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": "Improving the two above points will unlock huge performance boosts!", "raw": "Improving the two above points will unlock huge performance boosts!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Andrew NG says Research agents are already part of his workflow!", "raw": "Andrew NG says Research agents are already part of his 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": "𝗖𝗹𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀", "raw": "𝗖𝗹𝗼𝘀𝗶𝗻𝗴 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": "text", "value": "Andrew speculates that through agentic workflows, maybe generating many tokens fast from a small LLM will give better results than slower throughput from a powerful LLM like GPT-5.", "raw": "Andrew speculates that through agentic workflows, maybe generating many tokens fast from a small LLM will give better results than slower throughput from a powerful LLM like GPT-5.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🎬 Watch the talk here 👉 ", "raw": "🎬 Watch the talk 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=sal78ACtGTc", "href": "https://www.youtube.com/watch?v=sal78ACtGTc", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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've added his recommended reads to ", "raw": "📚 I've added his recommended reads to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/m-ric/agents-65ba776fbd9e29f771c07d4e", "href": null, "resource": { "type": "collection", "id": "m-ric/agents-65ba776fbd9e29f771c07d4e", "discussionNum": null }, "url": "https://huggingface.co/collections/m-ric/agents-65ba776fbd9e29f771c07d4e", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Aurora-M", "raw": "Aurora-M", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order", "raw": "The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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/2404.00399", "href": null, "resource": { "type": "paper", "id": "2404.00399", "discussionNum": null }, "url": "https://huggingface.co/papers/2404.00399", "code": null, "user": null, "label": "Aurora-M: The First Open Source Multilingual Language Model Red-teamed\n according to the U.S. Executive Order (2404.00399)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility. Initiatives such as BLOOM and StarCoder aim to democratize access to pretrained models for collaborative community development. However, such existing models face challenges: limited multilingual capabilities, continual pretraining causing catastrophic forgetting, whereas pretraining from scratch is computationally expensive, and compliance with AI safety and development laws. This paper presents Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code. Continually pretrained from StarCoderPlus on 435 billion additional tokens, Aurora-M surpasses 2 trillion tokens in total training token count. It is the first open-source multilingual model fine-tuned on human-reviewed safety instructions, thus aligning its development not only with conventional red-teaming considerations, but also with the specific concerns articulated in the Biden-Harris Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Aurora-M is rigorously evaluated across various tasks and languages, demonstrating robustness against catastrophic forgetting and outperforming alternatives in multilingual settings, particularly in safety evaluations. ", "raw": "Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility. Initiatives such as BLOOM and StarCoder aim to democratize access to pretrained models for collaborative community development. However, such existing models face challenges: limited multilingual capabilities, continual pretraining causing catastrophic forgetting, whereas pretraining from scratch is computationally expensive, and compliance with AI safety and development laws. This paper presents Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code. Continually pretrained from StarCoderPlus on 435 billion additional tokens, Aurora-M surpasses 2 trillion tokens in total training token count. It is the first open-source multilingual model fine-tuned on human-reviewed safety instructions, thus aligning its development not only with conventional red-teaming considerations, but also with the specific concerns articulated in the Biden-Harris Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Aurora-M is rigorously evaluated across various tasks and languages, demonstrating robustness against catastrophic forgetting and outperforming alternatives in multilingual settings, particularly in safety evaluations. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": 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": "How would you benchmark performance estimation algorithms vs data drift signals?", "raw": "How would you benchmark performance estimation algorithms vs data drift signals?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 working on a benchmarking analysis, and I'm currently doing the following:", "raw": "I'm working on a benchmarking analysis, and I'm currently doing the following:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Get univariate and multivariate drift signals and measure their correlation with realized performance.", "raw": "- Get univariate and multivariate drift signals and measure their correlation with realized performance.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Use drift signals as features of a regression model to predict the model's performance.", "raw": "- Use drift signals as features of a regression model to predict the model's performance.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Use drift signals as features of a classification model to predict a performance drop.", "raw": "- Use drift signals as features of a classification model to predict a performance drop.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 all the above experiments with results from Performance Estimation algorithms.", "raw": "- Compare all the above experiments with results from Performance Estimation algorithms.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 other ideas?", "raw": "Any other ideas?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🥇Open CoT Leaderboard", "raw": "🥇Open CoT 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": "We're delighted to announce the [Open CoT Leaderboard](", "raw": "We're delighted to announce the [Open CoT Leaderboard](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/logikon/open_cot_leaderboard", "href": null, "resource": { "type": "space", "id": "logikon/open_cot_leaderboard", "discussionNum": null }, "url": "https://huggingface.co/spaces/logikon/open_cot_leaderboard", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") on 🤗 Spaces.", "raw": ") on 🤗 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": "Unlike other LLM performance leaderboards, the Open CoT Leaderboard is not tracking absolute benchmark accuracies, but relative **accuracy gains** due to **chain-of-thought**.", "raw": "Unlike other LLM performance leaderboards, the Open CoT Leaderboard is not tracking absolute benchmark accuracies, but relative **accuracy gains** due to **chain-of-thought**.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Eval datasets that underpin the leaderboard are hosted [here](", "raw": "Eval datasets that underpin the leaderboard are hosted [here](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/cot-leaderboard", "href": "https://huggingface.co/cot-leaderboard", "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": "Feedback and suggestions more than welcome.", "raw": "Feedback and suggestions more than welcome.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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": "@clefourrier", "href": null, "resource": null, "url": null, "code": null, "user": "clefourrier", "label": null, "lang": null } ]

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[ { "type": "text", "value": "We would like to announce our Aurora-M multilingual models which is based on Starcoderplus.", "raw": "We would like to announce our Aurora-M multilingual models which is based on Starcoderplus.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Twitter: ", "raw": "Twitter: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://twitter.com/ontocord/status/1772778544051155029", "href": "https://twitter.com/ontocord/status/1772778544051155029", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "LinkedIn: ", "raw": "LinkedIn: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.linkedin.com/feed/update/urn:li:activity:7178521998845759488/", "href": "https://www.linkedin.com/feed/update/urn:li:activity:7178521998845759488/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 post: ", "raw": "Blog post: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/mayank-mishra/aurora", "href": "https://huggingface.co/blog/mayank-mishra/aurora", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Arxiv: ", "raw": "Arxiv: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2404.00399", "href": null, "resource": { "type": "paper", "id": "2404.00399", "discussionNum": null }, "url": "https://huggingface.co/papers/2404.00399", "code": null, "user": null, "label": "Aurora-M: The First Open Source Multilingual Language Model Red-teamed\n according to the U.S. Executive Order (2404.00399)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Current LLMs are very susceptible to generating toxic, harmful and even dangerous content. They can also generate outputs with gender or racial biases. The Biden-Harris Executive Order ", "raw": "Current LLMs are very susceptible to generating toxic, harmful and even dangerous content. They can also generate outputs with gender or racial biases. The Biden-Harris Executive Order ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence", "href": "https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") sets forth guidelines on what is considered a safe AI system.", "raw": ") sets forth guidelines on what is considered a safe AI 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": "text", "value": "Following up on these guidelines, we present the world's first open source Biden-Harris Executive Order Red teamed Multilingual Language Model: Aurora-M. Inspired by BigScience, the model is trained on 5 languages: English, Hindi, Japanese, Vietnamese and Finnish.", "raw": "Following up on these guidelines, we present the world's first open source Biden-Harris Executive Order Red teamed Multilingual Language Model: Aurora-M. Inspired by BigScience, the model is trained on 5 languages: English, Hindi, Japanese, Vietnamese and Finnish.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Red teamed model: ", "raw": "* Red teamed model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/aurora-m/aurora-m-biden-harris-redteamed(safety", "href": null, "resource": { "type": "model", "id": "aurora-m/aurora-m-biden-harris-redteamed", "discussionNum": null }, "url": "https://huggingface.co/aurora-m/aurora-m-biden-harris-redteamed(safety", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " tuned according to the order mentioned above)", "raw": " tuned according to the order mentioned above)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Base model: ", "raw": "* Base model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/aurora-m/aurora-m-base", "href": null, "resource": { "type": "model", "id": "aurora-m/aurora-m-base", "discussionNum": null }, "url": "https://huggingface.co/aurora-m/aurora-m-base", "code": null, "user": null, "label": null, "lang": 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[ { "type": "text", "value": "⚡ AutoQuant", "raw": "⚡ AutoQuant", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "AutoQuant is the evolution of my previous AutoGGUF notebook (", "raw": "AutoQuant is the evolution of my previous AutoGGUF 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/1P646NEg33BZy4BfLDNpTz0V0lwIU3CHu", "href": "https://colab.research.google.com/drive/1P646NEg33BZy4BfLDNpTz0V0lwIU3CHu", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "). It allows you to quantize your models in five different formats:", "raw": "). It allows you to quantize your models in five different formats:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- GGUF: perfect for inference on CPUs (and LM Studio)", "raw": "- GGUF: perfect for inference on CPUs (and LM Studio)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- GPTQ/EXL2: fast inference on GPUs", "raw": "- GPTQ/EXL2: fast inference on GPUs", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- AWQ: super fast inference on GPUs with vLLM (", "raw": "- AWQ: super fast inference on GPUs with vLLM (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/vllm-project/vllm", "href": "https://github.com/vllm-project/vllm", "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": "- HQQ: extreme quantization with decent 2-bit and 3-bit models", "raw": "- HQQ: extreme quantization with decent 2-bit and 3-bit 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": "Once the model is converted, it automatically uploads it on the Hugging Face Hub. To quantize a 7B model, GGUF only needs a T4 GPU, while the other methods require an A100 GPU.", "raw": "Once the model is converted, it automatically uploads it on the Hugging Face Hub. To quantize a 7B model, GGUF only needs a T4 GPU, while the other methods require an A100 GPU.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 an example of a model I quantized using HQQ and AutoQuant: ", "raw": "Here's an example of a model I quantized using HQQ and AutoQuant: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/mlabonne/AlphaMonarch-7B-2bit-HQQ", "href": null, "resource": { "type": "model", "id": "mlabonne/AlphaMonarch-7B-2bit-HQQ", "discussionNum": null }, "url": "https://huggingface.co/mlabonne/AlphaMonarch-7B-2bit-HQQ", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 hope you'll enjoy it and quantize lots of models! :)", "raw": "I hope you'll enjoy it and quantize lots of 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": "💻 AutoQuant: ", "raw": "💻 AutoQuant: ", "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/1b6nqC7UZVt8bx4MksX7s656GXPM-eWw4", "href": "https://colab.research.google.com/drive/1b6nqC7UZVt8bx4MksX7s656GXPM-eWw4", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Check out our work Symbol-LLM ! We have open-sourced both 7B / 13B model weights, as well as part of the symbolic collections ! Try it !", "raw": "Check out our work Symbol-LLM ! We have open-sourced both 7B / 13B model weights, as well as part of the symbolic collections ! Try 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": "Paper link: ", "raw": "Paper link: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2311.09278", "href": null, "resource": { "type": "paper", "id": "2311.09278", "discussionNum": null }, "url": "https://huggingface.co/papers/2311.09278", "code": null, "user": null, "label": "Symbol-LLM: Towards Foundational Symbol-centric Interface For Large\n Language Models (2311.09278)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 weights: ", "raw": "Model weights: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Symbol-LLM/Symbol-LLM-7B-Instruct", "href": null, "resource": { "type": "model", "id": "Symbol-LLM/Symbol-LLM-7B-Instruct", "discussionNum": null }, "url": "https://huggingface.co/Symbol-LLM/Symbol-LLM-7B-Instruct", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Introducing Indic Chat!", "raw": "Introducing Indic Chat!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 out best opensource Indic LLMs now on ", "raw": "Try out best opensource Indic LLMs now on ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.indic.chat/", "href": "https://www.indic.chat/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 available:", "raw": "Models available:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "• Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0", "raw": "• Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0", "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": "text", "value": "• GenVRadmin/AryaBhatta-GemmaOrca", "raw": "• GenVRadmin/AryaBhatta-GemmaOrca", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "• BhabhaAI/Gajendra-v0.1", "raw": "• BhabhaAI/Gajendra-v0.1", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "• ai4bharat/Airavata", "raw": "• ai4bharat/Airavata", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Additionally:", "raw": "Additionally:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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. We open up our discord for everyone to collaborate & accelerate Indic LLMs: ", "raw": "1. We open up our discord for everyone to collaborate & accelerate Indic LLMs: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://bhabha.ai/discord", "href": "https://bhabha.ai/discord", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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. We release ~600K rows filtered & Hindi translated version of OpenHermes-2.5 instruction dataset: ", "raw": "2. We release ~600K rows filtered & Hindi translated version of OpenHermes-2.5 instruction dataset: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/BhabhaAI/openhermes-2.5-hindi", "href": null, "resource": { "type": "dataset", "id": "BhabhaAI/openhermes-2.5-hindi", "discussionNum": null }, "url": "https://huggingface.co/datasets/BhabhaAI/openhermes-2.5-hindi", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Also, thanks to our compute sponsor - Telugu LLM Labs & Bhabha AI in helping us serve models for Indic Chat.", "raw": "Also, thanks to our compute sponsor - Telugu LLM Labs & Bhabha AI in helping us serve models for Indic Chat.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 be a sponsor too, checkout", "raw": "If you’d like to be a sponsor too, checkout", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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.indic.chat/sponsor", "href": "https://www.indic.chat/sponsor", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "On evaluating fine tuned 7B Italian open source LLMs I have collected many data points and I created a super simple explorative analyses. My hypothesis based on data are:", "raw": "On evaluating fine tuned 7B Italian open source LLMs I have collected many data points and I created a super simple explorative analyses. My hypothesis based on data are:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - mmlu is hard to improve when fine tuning a base model on a different language ", "raw": " - mmlu is hard to improve when fine tuning a base model on a different language ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 also on single GPUs can improve by 5% to 10% the base model on common tasks but a lot more on specific cases with the right training time and data", "raw": "- fine tuning also on single GPUs can improve by 5% to 10% the base model on common tasks but a lot more on specific cases with the right training time and 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": "- fine tuning can specialize well but at cost of loosing some foundational knowledge. ", "raw": "- fine tuning can specialize well but at cost of loosing some foundational 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": "Here the data ", "raw": "Here the data ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://docs.google.com/spreadsheets/d/1MBcxy1loK8eIycZG4DN84Q2ejZ0jSjxUBgoShHDR6IY/edit?usp=sharing", "href": "https://docs.google.com/spreadsheets/d/1MBcxy1loK8eIycZG4DN84Q2ejZ0jSjxUBgoShHDR6IY/edit?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": "Here the colab ", "raw": "Here the colab ", "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/1ra4_skG5QYWSYOzvagOoIoj4bibQD8Gw?usp=sharing", "href": "https://colab.research.google.com/drive/1ra4_skG5QYWSYOzvagOoIoj4bibQD8Gw?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": "Here an article with some considerations ", "raw": "Here an article with some considerations ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://medium.com/@giuxale/an-analyses-on-italian-llms-models-evaluations-51bffe1d44d1", "href": "https://medium.com/@giuxale/an-analyses-on-italian-llms-models-evaluations-51bffe1d44d1", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": 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": "Google DeepMind introduces Gecko a new text embedding! Gecko uses a two-step process that leverages synthetic data generation and reranking.", "raw": "Google DeepMind introduces Gecko a new text embedding! Gecko uses a two-step process that leverages synthetic data generation and reranking.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Keypoints:", "raw": "Keypoints:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 LLM to generate diverse synthetic queries and tasks from web passages", "raw": "* Uses an LLM to generate diverse synthetic queries and tasks from web passages", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Refines the data by retrieving candidate passages and relabeling positives/negatives using the same LLM", "raw": "* Refines the data by retrieving candidate passages and relabeling positives/negatives using the same 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": "* Achieves very good results on the Massive Text Embedding Benchmark, where compact 256D Gecko outperforms 768D models.", "raw": "* Achieves very good results on the Massive Text Embedding Benchmark, where compact 256D Gecko outperforms 768D 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": "text", "value": "* 768D Gecko achieves state-of-the-art performance competing with models a lot larger larger.", "raw": "* 768D Gecko achieves state-of-the-art performance competing with models a lot larger larger.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/2403.20327", "href": null, "resource": { "type": "paper", "id": "2403.20327", "discussionNum": null }, "url": "https://huggingface.co/papers/2403.20327", "code": null, "user": null, "label": "Gecko: Versatile Text Embeddings Distilled from Large Language Models (2403.20327)", "lang": null }, { "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 details in my blog: ", "raw": "More details in my blog: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/vladbogo/gecko", "href": "https://huggingface.co/blog/vladbogo/gecko", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 authors for their work!", "raw": "Congrats to the authors for their work!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Jamba", "raw": "Jamba", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Hybrid Transformer-Mamba Language Model", "raw": "A Hybrid Transformer-Mamba Language 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": "resource", "value": null, "raw": "https://huggingface.co/papers/2403.19887", "href": null, "resource": { "type": "paper", "id": "2403.19887", "discussionNum": null }, "url": "https://huggingface.co/papers/2403.19887", "code": null, "user": null, "label": "Jamba: A Hybrid Transformer-Mamba Language Model (2403.19887)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 present Jamba, a new base large language model based on a novel hybrid Transformer-Mamba mixture-of-experts (MoE) architecture. Specifically, Jamba interleaves blocks of Transformer and Mamba layers, enjoying the benefits of both model families. MoE is added in some of these layers to increase model capacity while keeping active parameter usage manageable. This flexible architecture allows resource- and objective-specific configurations. In the particular configuration we have implemented, we end up with a powerful model that fits in a single 80GB GPU. Built at large scale, Jamba provides high throughput and small memory footprint compared to vanilla Transformers, and at the same time state-of-the-art performance on standard language model benchmarks and long-context evaluations. Remarkably, the model presents strong results for up to 256K tokens context length. We study various architectural decisions, such as how to combine Transformer and Mamba layers, and how to mix experts, and show that some of them are crucial in large scale modeling. We also describe several interesting properties of these architectures which the training and evaluation of Jamba have revealed, and plan to release checkpoints from various ablation runs, to encourage further exploration of this novel architecture. We make the weights of our implementation of Jamba publicly available under a permissive license.", "raw": "We present Jamba, a new base large language model based on a novel hybrid Transformer-Mamba mixture-of-experts (MoE) architecture. Specifically, Jamba interleaves blocks of Transformer and Mamba layers, enjoying the benefits of both model families. MoE is added in some of these layers to increase model capacity while keeping active parameter usage manageable. This flexible architecture allows resource- and objective-specific configurations. In the particular configuration we have implemented, we end up with a powerful model that fits in a single 80GB GPU. Built at large scale, Jamba provides high throughput and small memory footprint compared to vanilla Transformers, and at the same time state-of-the-art performance on standard language model benchmarks and long-context evaluations. Remarkably, the model presents strong results for up to 256K tokens context length. We study various architectural decisions, such as how to combine Transformer and Mamba layers, and how to mix experts, and show that some of them are crucial in large scale modeling. We also describe several interesting properties of these architectures which the training and evaluation of Jamba have revealed, and plan to release checkpoints from various ablation runs, to encourage further exploration of this novel architecture. We make the weights of our implementation of Jamba publicly available under a permissive 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 } ]

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[ { "type": "text", "value": "🎯🖼️🌟 New Research Alert - ICLR 2024! 🌟 🖼️🎯", "raw": "🎯🖼️🌟 New Research Alert - ICLR 2024! 🌟 🖼️🎯", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Title: Adversarial AutoMixup 🖼️", "raw": "📄 Title: Adversarial AutoMixup 🖼️", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📝 Description: Adversarial AutoMixup is an approach to image classification augmentation. By alternately optimizing a classifier and a mixed-sample generator, it attempts to generate challenging samples and improve the robustness of the classifier against overfitting.", "raw": "📝 Description: Adversarial AutoMixup is an approach to image classification augmentation. By alternately optimizing a classifier and a mixed-sample generator, it attempts to generate challenging samples and improve the robustness of the classifier against overfitting.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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: Huafeng Qin et al.", "raw": "👥 Authors: Huafeng Qin 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": "📅 Conference: ICLR, May 7-11, 2024 | Vienna, Austria 🇦🇹", "raw": "📅 Conference: ICLR, May 7-11, 2024 | Vienna, Austria 🇦🇹", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/2312.11954", "href": null, "resource": { "type": "paper", "id": "2312.11954", "discussionNum": null }, "url": "https://huggingface.co/papers/2312.11954", "code": null, "user": null, "label": "Adversarial AutoMixup (2312.11954)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📁 Repository: ", "raw": "📁 Repository: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/JinXins/Adversarial-AutoMixup", "href": "https://github.com/JinXins/Adversarial-AutoMixup", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Papers: more cutting-edge research presented at other conferences in the ", "raw": "📚 More Papers: more cutting-edge research presented at other conferences in the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "href": null, "resource": { "type": "space", "id": "DmitryRyumin/NewEraAI-Papers", "discussionNum": null }, "url": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " curated by ", "raw": " curated by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@DmitryRyumin", "href": null, "resource": null, "url": null, "code": null, "user": "DmitryRyumin", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🔍 Keywords: #AutoMixup #ImageClassification #ImageAugmentation #AdversarialLearning #ICLR2024 #DeepLearning #Innovation", "raw": "🔍 Keywords: #AutoMixup #ImageClassification #ImageAugmentation #AdversarialLearning #ICLR2024 #DeepLearning #Innovation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Diaries of Open Source. Part 13 🤗", "raw": "Diaries of Open Source. 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[ { "type": "text", "value": "This is the closest I’ve seen of a scalable AI/LLM Operating System - it has all the major ingredients of a feasible AI OS 1 architecture:", "raw": "This is the closest I’ve seen of a scalable AI/LLM Operating System - it has all the major ingredients of a feasible AI OS 1 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": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Extends classical OS functionalities with an LLM Kernel.", "raw": "- Extends classical OS functionalities with an LLM Kernel.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Multi agent-centric approach.", "raw": "- Multi agent-centric 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": "text", "value": "- Optimized resource allocation system that allows for LLM-based tasks and Classical OS tasks to coexist.", "raw": "- Optimized resource allocation system that allows for LLM-based tasks and Classical OS tasks to coexist.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Agent Scheduler that can perform classical os operations (FIFO, RR).", "raw": "- An Agent Scheduler that can perform classical os operations (FIFO, RR).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Context Manager to improve alignment.", "raw": "- A Context Manager to improve alignment.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Lazy Memory Manager for agents (ensures data is stored and accessible only while the agent is active)", "raw": "- Lazy Memory Manager for agents (ensures data is stored and accessible only while the agent is active)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Enhanced security module for the AI-driven environment.", "raw": "- An Enhanced security module for the AI-driven 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": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It does hit all checkpoints, doesn’t it? An upscale version of ", "raw": "It does hit all checkpoints, doesn’t it? An upscale version of ", "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.", "raw": "’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": "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/agiresearch/AIOS", "href": "https://github.com/agiresearch/AIOS", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🚀🚀 Exciting times for the document AI community! ", "raw": "🚀🚀 Exciting times for the document AI 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": "We're thrilled to announce the release of some of the largest OCR datasets available to the public.", "raw": "We're thrilled to announce the release of some of the largest OCR datasets available to the public.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🔥 With over 26 million pages , 18 billion text tokens, and 6TB of data, these resources are a significant leap forward for document AI research.", "raw": "🔥 With over 26 million pages , 18 billion text tokens, and 6TB of data, these resources are a significant leap forward for document AI research.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 how to access these datasets quickly:", "raw": "Here's how to access these datasets quickly:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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": "```\nfrom datasets import load_dataset\n\npdfa_dataset = load_dataset('pixparse/pdfa-eng-wds', streaming=True)\nIDL_dataset = load_dataset('pixparse/idl-wds', streaming=True)\n```", "href": null, "resource": null, "url": null, "code": "from datasets import load_dataset\n\npdfa_dataset = load_dataset('pixparse/pdfa-eng-wds', streaming=True)\nIDL_dataset = load_dataset('pixparse/idl-wds', streaming=True)", "user": null, "label": null, "lang": null }, { "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 enables you to stream them directly, integrating seamlessly with your projects using the Hugging Face datasets library. On the hub, you can find them here:", "raw": "This enables you to stream them directly, integrating seamlessly with your projects using the Hugging Face datasets library. On the hub, you can find them 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": "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/pixparse/pdfa-eng-wds", "href": null, "resource": { "type": "dataset", "id": "pixparse/pdfa-eng-wds", "discussionNum": null }, "url": "https://huggingface.co/datasets/pixparse/pdfa-eng-wds", "code": null, "user": null, "label": null, "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/pixparse/idl-wds", "href": null, "resource": { "type": "dataset", "id": "pixparse/idl-wds", "discussionNum": null }, "url": "https://huggingface.co/datasets/pixparse/idl-wds", "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": "For lean data loading, the new [chug](", "raw": "For lean data loading, the new [chug](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/huggingface/chug", "href": "https://github.com/huggingface/chug", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") library offers a solution with pdf decoding:", "raw": ") library offers a solution with pdf decoding:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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": "```\nimport chug\n\ntask_cfg = chug.DataTaskDocReadCfg(\n page_sampling='all',\n)\ndata_cfg = chug.DataCfg(\n source='pixparse/pdfa-eng-wds',\n split='train',\n batch_size=None,\n format='hfids',\n num_workers=0,\n)\ndata_loader = chug.create_loader(\n data_cfg,\n task_cfg,\n)\nsample = next(iter(data_loader))\n```", "href": null, "resource": null, "url": null, "code": "import chug\n\ntask_cfg = chug.DataTaskDocReadCfg(\n page_sampling='all',\n)\ndata_cfg = chug.DataCfg(\n source='pixparse/pdfa-eng-wds',\n split='train',\n batch_size=None,\n format='hfids',\n num_workers=0,\n)\ndata_loader = chug.create_loader(\n data_cfg,\n task_cfg,\n)\nsample = next(iter(data_loader))", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 owe a huge thank you to Peter Wyatt, Kate Tasker, Rachel Taketa, Ali Furkan Biten, Ruben Tito, and their colleagues for their contributions. Their work putting these datasets together has been invaluable. 🤗", "raw": "We owe a huge thank you to Peter Wyatt, Kate Tasker, Rachel Taketa, Ali Furkan Biten, Ruben Tito, and their colleagues for their contributions. Their work putting these datasets together has been invaluable. 🤗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Ahead:", "raw": "Looking Ahead:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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're on a mission to enhance document AI capabilities, and these datasets are just the beginning. With your engagement and innovation, we're confident in the community's ability to develop robust OCR solutions. We encourage you to explore these datasets, experiment with the code, and contribute to the collective progress in document AI.", "raw": "We're on a mission to enhance document AI capabilities, and these datasets are just the beginning. With your engagement and innovation, we're confident in the community's ability to develop robust OCR solutions. We encourage you to explore these datasets, experiment with the code, and contribute to the collective progress in document 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": "text", "value": "For detailed information on usage and licensing, please refer to the dataset cards on the Hugging Face hub.", "raw": "For detailed information on usage and licensing, please refer to the dataset cards on the Hugging Face hub.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": 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": "Happy to share Living Images and the demo video of the product outpainting model behind it 🚀", "raw": "Happy to share Living Images and the demo video of the product outpainting model behind 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": "Send your generation requests in the thread 🧵or use ", "raw": "Send your generation requests in the thread 🧵or use ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://img.coframe.ai", "href": "https://img.coframe.ai", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Fun fact about evaluation!", "raw": "Fun fact about evaluation!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Did you know that, if you evaluate the same model, with the same prompt formatting & the same fixed few-shot examples, only changing ", "raw": "Did you know that, if you evaluate the same model, with the same prompt formatting & the same fixed few-shot examples, only changing ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 order in which the few shot examples are added to the prompt ♻️", "raw": "♻️the order in which the few shot examples are added to the prompt ♻️", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 a difference of up to 3 points in evaluation score? ", "raw": "you get a difference of up to 3 points in evaluation score? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 did a small experiment using some MMLU subsets on the best performing 7B and lower pretrained models from the leaderboard.", "raw": "I did a small experiment using some MMLU subsets on the best performing 7B and lower pretrained models from the 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": "I tried 8 different prompting methods (containing more or less information, such as just the question, or Question: question, or Question: question Choices: ..., see the x axis) that are commonly used in evaluation.", "raw": "I tried 8 different prompting methods (containing more or less information, such as just the question, or Question: question, or Question: question Choices: ..., see the x axis) that are commonly used in evaluation.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 then compared the results for all these methods, in 5-shot, during 2 runs. The *only difference* between the first and second run being that the samples used in few-shot are not introduced in the same order.", "raw": "I then compared the results for all these methods, in 5-shot, during 2 runs. The *only difference* between the first and second run being that the samples used in few-shot are not introduced in the same order.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 example, run one would have been \"A B C D E Current sample\", vs, in run 2, \"D C E A B Current sample\". ", "raw": "For example, run one would have been \"A B C D E Current sample\", vs, in run 2, \"D C E A B Current sample\". ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 the other experiment parameters stayed exactly the same.", "raw": "All the other experiment parameters stayed exactly the same.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 you can see on the attached picture, you get a difference of up to 3 points between the 2 few-shot samples shuffling.", "raw": "As you can see on the attached picture, you get a difference of up to 3 points between the 2 few-shot samples shuffling.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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, when just changing *the order of the few shot samples* can change your results by several points, what is the impact of all other \"minimal\" and unreported prompting changes?", "raw": "So, when just changing *the order of the few shot samples* can change your results by several points, what is the impact of all other \"minimal\" and unreported prompting changes?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 kind of model score, provided without an evaluation script for reproducibility, is basically bullshit (or coms).", "raw": "-> Any kind of model score, provided without an evaluation script for reproducibility, is basically bullshit (or coms).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 why we need reproducible evaluation in a fair and exactly similar setup, using evaluation suites such as ", "raw": "-> This is why we need reproducible evaluation in a fair and exactly similar setup, using evaluation suites such as ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`lm_eval`", "href": null, "resource": null, "url": null, "code": "lm_eval", "user": null, "label": null, "lang": null }, { "type": "text", "value": " from the Harness, ", "raw": " from the Harness, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`lighteval`", "href": null, "resource": null, "url": null, "code": "lighteval", "user": null, "label": null, "lang": null }, { "type": "text", "value": " from HF, or the Open LLM Leaderboard.", "raw": " from HF, or the Open LLM Leaderboard.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Teraflop AI is excited to help support the Caselaw Access Project and Harvard Library Innovation Lab, in the release of over 6.6 million state and federal court decisions published throughout U.S. history. It is important to democratize fair access to data to the public, legal community, and researchers. This is a processed and cleaned version of the original CAP data.", "raw": "Teraflop AI is excited to help support the Caselaw Access Project and Harvard Library Innovation Lab, in the release of over 6.6 million state and federal court decisions published throughout U.S. history. It is important to democratize fair access to data to the public, legal community, and researchers. This is a processed and cleaned version of the original CAP 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": "During the digitization of these texts, there were erroneous OCR errors that occurred. We worked to post-process each of the texts for model training to fix encoding, normalization, repetition, redundancy, parsing, and formatting.", "raw": "During the digitization of these texts, there were erroneous OCR errors that occurred. We worked to post-process each of the texts for model training to fix encoding, normalization, repetition, redundancy, parsing, and formatting.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Teraflop AI’s data engine allows for the massively parallel processing of web-scale datasets into cleaned text form. ", "raw": "Teraflop AI’s data engine allows for the massively parallel processing of web-scale datasets into cleaned text 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": "Link to the processed dataset: ", "raw": "Link to the processed dataset: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/datasets/TeraflopAI/Caselaw_Access_Project", "href": "https://huggingface.co/datasets/TeraflopAI/Caselaw_Access_Project", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Caselaw Access Project dataset is licensed under the CC0 License.", "raw": "The Caselaw Access Project dataset is licensed under the CC0 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": "We plan to release trillions of commercially licensed text tokens, images, audio, videos, and other datasets spanning numerous domains and modalities over the next months. If you are interested in contributing commercially licensed data be sure to reach out: ", "raw": "We plan to release trillions of commercially licensed text tokens, images, audio, videos, and other datasets spanning numerous domains and modalities over the next months. If you are interested in contributing commercially licensed data be sure to reach out: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://twitter.com/EnricoShippole", "href": "https://twitter.com/EnricoShippole", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Follow us for the next collaborative dataset releases: ", "raw": "Follow us for the next collaborative dataset releases: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://twitter.com/TeraflopAI", "href": "https://twitter.com/TeraflopAI", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "New - add your bluesky account to your HF profile:", "raw": "New - add your bluesky account to your HF profile:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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/settings/profile", "href": "https://huggingface.co/settings/profile", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Is the grass greener, the sky bluer? Will try and figure it out at ", "raw": "Is the grass greener, the sky bluer? Will try and figure it out at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://bsky.app/profile/jeffboudier.bsky.social", "href": "https://bsky.app/profile/jeffboudier.bsky.social", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "By the way, HF people starter pack ", "raw": "By the way, HF people starter pack ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://bsky.app/starter-pack/huggingface.bsky.social/3laz5x7naiz22", "href": "https://bsky.app/starter-pack/huggingface.bsky.social/3laz5x7naiz22", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Weekend Dribble 📦🍺", "raw": "Weekend Dribble 📦🍺", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Adapters for Product Ad Backdrops, Smooth Polaroids, Minimalist Sketch cards, Super Blends!!", "raw": "Adapters for Product Ad Backdrops, Smooth Polaroids, Minimalist Sketch cards, Super Blends!!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 on: ", "raw": "🤏Demo on: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC", "href": null, "resource": { "type": "space", "id": "prithivMLmods/FLUX-LoRA-DLC", "discussionNum": null }, "url": "https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Stranger Zones : ", "raw": "Stranger Zones : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Blend } : ", "raw": "👉🏼{ Super Blend } : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/strangerzonehf/Flux-Super-Blend-LoRA", "href": null, "resource": { "type": "model", "id": "strangerzonehf/Flux-Super-Blend-LoRA", "discussionNum": null }, "url": "https://huggingface.co/strangerzonehf/Flux-Super-Blend-LoRA", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", 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[ { "type": "text", "value": "Can language models replace developers? #RepoCod says “Not Yet”, because GPT-4o and other LLMs have <30% accuracy/pass@1 on real-world code generation tasks. ", "raw": "Can language models replace developers? #RepoCod says “Not Yet”, because GPT-4o and other LLMs have <30% accuracy/pass@1 on real-world code generation 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": "- Leaderboard ", "raw": "- Leaderboard ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://lt-asset.github.io/REPOCOD/", "href": "https://lt-asset.github.io/REPOCOD/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/lt-asset/REPOCOD", "href": null, "resource": { "type": "dataset", "id": "lt-asset/REPOCOD", "discussionNum": null }, "url": "https://huggingface.co/datasets/lt-asset/REPOCOD", "code": null, "user": null, "label": null, "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": "@jiang719", "href": null, "resource": null, "url": null, "code": null, "user": "jiang719", "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": "@shanchao", "href": null, "resource": null, "url": null, "code": null, "user": "shanchao", "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": "@Yiran-Hu1007", "href": null, "resource": null, "url": null, "code": null, "user": "Yiran-Hu1007", "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": "Compared to #SWEBench, RepoCod tasks are", "raw": "Compared to #SWEBench, RepoCod tasks are", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- General code generation tasks, while SWE-Bench tasks resolve pull requests from GitHub issues.", "raw": "- General code generation tasks, while SWE-Bench tasks resolve pull requests from GitHub issues.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- With 2.6X more tests per task (313.5 compared to SWE-Bench’s 120.8).", "raw": "- With 2.6X more tests per task (313.5 compared to SWE-Bench’s 120.8).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Compared to #HumanEval, #MBPP, #CoderEval, and #ClassEval, RepoCod has 980 instances from 11 Python projects, with", "raw": "Compared to #HumanEval, #MBPP, #CoderEval, and #ClassEval, RepoCod has 980 instances from 11 Python projects, 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": "- Whole function generation", "raw": "- Whole function 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": "text", "value": "- Repository-level context", "raw": "- Repository-level context", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Validation with test cases, and ", "raw": "- Validation with test cases, and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Real-world complex tasks: longest average canonical solution length (331.6 tokens) and the highest average cyclomatic complexity (9.00)", "raw": "- Real-world complex tasks: longest average canonical solution length (331.6 tokens) and the highest average cyclomatic complexity (9.00)", "href": null, "resource": null, "url": null, "code": null, 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file-level, and 66 self-contains 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": "- Detailed problem descriptions (967 tokens) and long canonical solutions (918 tokens)", "raw": "- Detailed problem descriptions (967 tokens) and long canonical solutions (918 tokens)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- GPT-4o and other LLMs have < 10% accuracy/pass@1 on RepoCod-Lite tasks. 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[ { "type": "text", "value": "🚀🚀🚀Introducing Insight-V! An early attempt towards o1-like multi-modal reasoning. ", "raw": "🚀🚀🚀Introducing Insight-V! An early attempt towards o1-like multi-modal 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": "We offer a structured long-chain visual reasoning data generation pipeline and a multi-agent system to unleash the reasoning potential of MLLMs.", "raw": "We offer a structured long-chain visual reasoning data generation pipeline and a multi-agent system to unleash the reasoning potential of 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": "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/2411.14432", "href": "https://arxiv.org/abs/2411.14432", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/dongyh20/Insight-V", "href": "https://github.com/dongyh20/Insight-V", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Weight: ", "raw": "💼 Model Weight: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/THUdyh/insight-v-673f5e1dd8ab5f2d8d332035", "href": null, "resource": { "type": "collection", "id": "THUdyh/insight-v-673f5e1dd8ab5f2d8d332035", "discussionNum": null }, "url": "https://huggingface.co/collections/THUdyh/insight-v-673f5e1dd8ab5f2d8d332035", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Made a new app to visualize the LLM race ⇒ 𝗡𝗼 𝗘𝘂𝗿𝗼𝗽𝗲𝗮𝗻 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗶𝗻 𝘁𝗵𝗲 𝘁𝗼𝗽 𝟭𝟬 🇪🇺❌", "raw": "Made a new app to visualize the LLM race ⇒ 𝗡𝗼 𝗘𝘂𝗿𝗼𝗽𝗲𝗮𝗻 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗶𝗻 𝘁𝗵𝗲 𝘁𝗼𝗽 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": "See the app here 👉 ", "raw": "See the app here 👉 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/m-ric/llm-race-to-the-top", "href": null, "resource": { "type": "space", "id": "m-ric/llm-race-to-the-top", "discussionNum": null }, "url": "https://huggingface.co/spaces/m-ric/llm-race-to-the-top", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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've adapted an app by ", "raw": "I've adapted an app by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@andrewrreed", "href": null, "resource": null, "url": null, "code": null, "user": "andrewrreed", "label": null, "lang": null }, { "type": "text", "value": " that tracks progress of LLMs (", "raw": " that tracks progress of LLMs (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/andrewrreed/closed-vs-open-arena-elo", "href": null, "resource": { "type": "space", "id": "andrewrreed/closed-vs-open-arena-elo", "discussionNum": null }, "url": "https://huggingface.co/spaces/andrewrreed/closed-vs-open-arena-elo", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "), on the Chatbot Arena leaderboard, to compare companies from different countries.", "raw": "), on the Chatbot Arena leaderboard, to compare companies from different countries.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 outcome is quite sad, as a Frenchman and European.", "raw": "The outcome is quite sad, as a Frenchman and European.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 top 10 is exclusively US 🇺🇸 and Chinese 🇨🇳 companies (after great Chinese LLM releases recently, like the Qwen2.5 series), with the notable exception of Mistral AI 🇫🇷.", "raw": "The top 10 is exclusively US 🇺🇸 and Chinese 🇨🇳 companies (after great Chinese LLM releases recently, like the Qwen2.5 series), with the notable exception of Mistral 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": "text", "value": "American companies are making fast progress, Chinese ones even faster. Europe is at risk of being left behind. And the EU AI Act hasn't even come into force yet to slow down the EU market. We need to wake up 😬", "raw": "American companies are making fast progress, Chinese ones even faster. Europe is at risk of being left behind. And the EU AI Act hasn't even come into force yet to slow down the EU market. We need to wake up 😬", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "⚠️ Caution: This Chatbot Arena ELO ranking is not the most accurate, especially at high scores like this, because LLM makers can game it to some extent.", "raw": "⚠️ Caution: This Chatbot Arena ELO ranking is not the most accurate, especially at high scores like this, because LLM makers can game it to some extent.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "What a week! A recap for everything you missed ❄️", "raw": "What a week! A recap for everything you missed ❄️", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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/collections/merve/nov-22-releases-673fbbcfc1c97c4f411def07", "href": null, "resource": { "type": "collection", "id": "merve/nov-22-releases-673fbbcfc1c97c4f411def07", "discussionNum": null }, "url": "https://huggingface.co/collections/merve/nov-22-releases-673fbbcfc1c97c4f411def07", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Multimodal ✨", "raw": "Multimodal ✨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "> Mistral AI", "raw": "> Mistral 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": "text", "value": " released Pixtral 124B, a gigantic open vision language model", "raw": " released Pixtral 124B, a gigantic open vision language 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": "text", "value": "> Llava-CoT (formerly known as Llava-o1) was released, a multimodal reproduction of o1 model by PKU", "raw": "> Llava-CoT (formerly known as Llava-o1) was released, a multimodal reproduction of o1 model by PKU", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "> OpenGVLab released MMPR: a new multimodal reasoning dataset", "raw": "> OpenGVLab released MMPR: a new multimodal reasoning 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": "> Jina has released Jina-CLIP-v2 0.98B multilingual multimodal embeddings", "raw": "> Jina has released Jina-CLIP-v2 0.98B multilingual multimodal 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": "text", "value": "> Apple released new SotA vision encoders AIMv2", "raw": "> Apple released new SotA vision encoders AIMv2", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 🦙", "raw": "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": "text", "value": "> AllenAI dropped a huge release of models, datasets and scripts for Tülu, a family of models based on Llama 3.1 aligned with SFT, DPO and a new technique they have developed called RLVR", "raw": "> AllenAI dropped a huge release of models, datasets and scripts for Tülu, a family of models based on Llama 3.1 aligned with SFT, DPO and a new technique they have developed called RLVR", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "> Jina has released embeddings-v3: new multilingual embeddings with longer context", "raw": "> Jina has released embeddings-v3: new 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orca-agentinstruct-1M-v1: a gigantic instruction dataset of 1M synthetic instruction 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": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Image Generation 🖼️", "raw": "Image 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": "text", "value": "> Black Forest Labs released Flux 1. tools: four new models for different image modifications and two LoRAs to do image conditioning and better steer generations", "raw": "> Black Forest Labs released Flux 1. tools: four new models for different image modifications and two LoRAs to do image conditioning and better steer generations", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Lastly Hugging Face released a new library Observers: a lightweight SDK for monitoring interactions with AI APIs and easily store and browse them 📚", "raw": "Lastly Hugging Face released a new library Observers: a lightweight SDK for monitoring interactions with AI APIs and easily store and browse 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": "text", "value": "$ pip install observers", "raw": "$ pip install observers", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": 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": "Excited to share my analysis of the most groundbreaking DCN-V2 paper from ", "raw": "Excited to share my analysis of the most groundbreaking DCN-V2 paper from ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Google", "href": null, "resource": null, "url": null, "code": null, "user": "Google", "label": null, "lang": null }, { "type": "text", "value": ", which introduces significant improvements to deep learning recommendation systems! ", "raw": ", which introduces significant improvements to deep learning recommendation systems! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 highlights: ", "raw": "Key technical highlights: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ">> Core Architecture ", "raw": ">> Core 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": "- Starts with an embedding layer that handles both sparse categorical and dense features ", "raw": "- Starts with an embedding layer that handles both sparse categorical and dense 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": "text", "value": "- Unique capability to handle variable embedding sizes from small to large vocabulary sizes ", "raw": "- Unique capability to handle variable embedding sizes from small to large vocabulary 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": "text", "value": "- Cross network creates explicit bounded-degree feature interactions ", "raw": "- Cross network creates explicit bounded-degree feature interactions ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Deep network complements with implicit feature interactions ", "raw": "- Deep network complements with implicit feature interactions ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 combination modes: stacked and parallel architectures ", "raw": "- Two combination modes: stacked and parallel architectures ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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": "- Enhanced cross layers with full matrix-based feature interaction learning instead of vector-based ", "raw": "- Enhanced cross layers with full matrix-based feature interaction learning instead of vector-based ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Mixture of Low-Rank architecture with: ", "raw": "- Mixture of Low-Rank architecture 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": " * Multiple expert networks learning in different subspaces ", "raw": " * Multiple expert networks learning in different subspaces ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " * Dynamic gating mechanism to adaptively combine experts ", "raw": " * Dynamic gating mechanism to adaptively combine 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": "text", "value": " * Efficient time complexity when specific conditions are met ", "raw": " * Efficient time complexity when specific conditions are met ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " * Support for non-linear transformations in projected spaces ", "raw": " * Support for non-linear transformations in projected 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": ">> Production Optimizations ", "raw": ">> Production Optimizations ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Low-rank matrix approximation leveraging singular value decay patterns ", "raw": "- Low-rank matrix approximation leveraging singular value decay patterns ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Mixture-of-Experts decomposition into smaller subspaces ", "raw": "- Mixture-of-Experts decomposition into smaller subspaces ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Efficient parameter allocation between cross and deep networks ", "raw": "- Efficient parameter allocation between cross and deep networks ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 feature interaction learning for higher-order interactions in multi-layered networks ", "raw": "- Automatic feature interaction learning for higher-order interactions in multi-layered networks ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Support for both homogeneous and heterogeneous polynomial patterns ", "raw": "- Support for both homogeneous and heterogeneous polynomial patterns ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ">> Real-World Impact ", "raw": ">> Real-World Impact ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Successfully deployed across Google's recommendation systems ", "raw": "- Successfully deployed across Google's recommendation systems ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Significant gains in both offline accuracy and online metrics ", "raw": "- Significant gains in both offline accuracy and online metrics ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 performance-latency tradeoffs through low-rank approximations ", "raw": "- Better performance-latency tradeoffs through low-rank approximations ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Proven effectiveness on large-scale data with billions of training examples ", "raw": "- Proven effectiveness on large-scale data with billions of training examples ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 represents a major leap forward in making deep learning recommendation systems more practical and efficient at scale. ", "raw": "This represents a major leap forward in making deep learning recommendation systems more practical and efficient at scale. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Thoughts? Would love to hear your experiences implementing similar architectures in production!", "raw": "Thoughts? Would love to hear your experiences implementing similar architectures in production!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Apple released AIMv2 🍏 a family of state-of-the-art open-set vision encoders", "raw": "Apple released AIMv2 🍏 a family of state-of-the-art open-set vision encoders", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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/collections/apple/aimv2-6720fe1558d94c7805f7688c", "href": null, "resource": { "type": "collection", "id": "apple/aimv2-6720fe1558d94c7805f7688c", "discussionNum": null }, "url": "https://huggingface.co/collections/apple/aimv2-6720fe1558d94c7805f7688c", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "> like CLIP, but add a decoder and train on autoregression 🤯", "raw": "> like CLIP, but add a decoder and train on autoregression 🤯", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "> 19 open models come in 300M, 600M, 1.2B, 2.7B with resolutions of 224, 336, 448", "raw": "> 19 open models come in 300M, 600M, 1.2B, 2.7B with resolutions of 224, 336, 448", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "> Load and use with 🤗 transformers", "raw": "> Load and use with 🤗 transformers", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Import any dataset from the Hub and configure your labeling tasks without needing any code!", "raw": "Import any dataset from the Hub and configure your labeling tasks without needing any code!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 excited about extending the Hugging Face Hub integration with many more streamlined features and workflows, and we would love to hear your feedback and ideas, so don't feel shy and reach out 🫶🏽", "raw": "Really excited about extending the Hugging Face Hub integration with many more streamlined features and workflows, and we would love to hear your feedback and ideas, so don't feel shy and reach 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": "link", "value": null, "raw": "https://huggingface.co/blog/argilla-ui-hub", "href": "https://huggingface.co/blog/argilla-ui-hub", "resource": null, "url": null, "code": null, "user": null, "label": 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": "Discovered an outrageous bug on the ChatGPT official website, especially for those using ad-blocking plugins. Please make sure to add ", "raw": "Discovered an outrageous bug on the ChatGPT official website, especially for those using ad-blocking plugins. Please make sure to add ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`browser-intake-datadoghq.com`", "href": null, "resource": null, "url": null, "code": "browser-intake-datadoghq.com", "user": null, "label": null, "lang": null }, { "type": "text", "value": " to your ad block whitelist. The ChatGPT webpage relies on this site for heartbeat detection, but since it belongs to an ad tracking network, it's included in major ad-blocking lists. (If you're using Clash, also remember to add it to the whitelist.) Failing to do so may cause the ChatGPT web interface to display a greyed-out send button after clicking, with no response.", "raw": " to your ad block whitelist. The ChatGPT webpage relies on this site for heartbeat detection, but since it belongs to an ad tracking network, it's included in major ad-blocking lists. (If you're using Clash, also remember to add it to the whitelist.) Failing to do so may cause the ChatGPT web interface to display a greyed-out send button after clicking, with no response.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 users with Chinese IP addresses, consider adding this URL to the rules of your U.S. node, as the response headers from this site will report the user's physical location to GPT.", "raw": "For users with Chinese IP addresses, consider adding this URL to the rules of your U.S. node, as the response headers from this site will report the user's physical location to GPT.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🎉 Celebrating One Year of #SauerkrautLM with Two Groundbreaking Releases! ", "raw": "🎉 Celebrating One Year of #SauerkrautLM with Two Groundbreaking Releases! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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're thrilled to announce the release of SauerkrautLM-v2-14b in two specialized versions: ", "raw": "We're thrilled to announce the release of SauerkrautLM-v2-14b in two specialized versions: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-SFT", "href": null, "resource": { "type": "model", "id": "VAGOsolutions/SauerkrautLM-v2-14b-SFT", "discussionNum": null }, "url": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-SFT", "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/VAGOsolutions/SauerkrautLM-v2-14b-DPO", "href": null, "resource": { "type": "model", "id": "VAGOsolutions/SauerkrautLM-v2-14b-DPO", "discussionNum": null }, "url": "https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ". Built on the robust Qwen2.5-14B foundation, these models represent a significant leap forward in multilingual AI capabilities.", "raw": ". Built on the robust Qwen2.5-14B foundation, these models represent a significant leap forward in multilingual AI capabilities.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🔬 Technical Breakthroughs:", "raw": "🔬 Technical Breakthroughs:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💠 Innovative three-phase Fine-Tuning approach", "raw": "💠 Innovative three-phase Fine-Tuning approach", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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-step Spectrum SFT + one-step Spectrum DPO optimization phase for enhanced performance", "raw": "💠 Two-step Spectrum SFT + one-step Spectrum DPO optimization phase for enhanced performance", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💠 Balance of German and English language capabilities", "raw": "💠 Balance of German and English language capabilities", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💠 Advanced function calling - almost on par with Claude-3.5-Sonnet-20240620", "raw": "💠 Advanced function calling - almost on par with Claude-3.5-Sonnet-20240620", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🇩🇪 German Language Excellence:", "raw": "🇩🇪 German Language Excellence:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 sets this release apart is our unique achievement in simultaneously improving both German and English capabilities. Through our specialized training approach with over 1.2B tokens across two phases, we've managed to:", "raw": "What sets this release apart is our unique achievement in simultaneously improving both German and English capabilities. Through our specialized training approach with over 1.2B tokens across two phases, we've managed to:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💠 Enhance German language understanding and generation (SFT Version > DPO Version)", "raw": "💠 Enhance German language understanding and generation (SFT Version > DPO Version)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💠 Maintain authentic German linguistic nuances", "raw": "💠 Maintain authentic German linguistic nuances", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💠 Improve cross-lingual capabilities", "raw": "💠 Improve cross-lingual capabilities", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💠 Preserve cultural context awareness", "raw": "💠 Preserve cultural context awareness", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": 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"lang": null }, { "type": "text", "value": "💠 Mathematics-focused content (proprietary classifier-selected)", "raw": "💠 Mathematics-focused content (proprietary classifier-selected)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💠 High-quality German training data", "raw": "💠 High-quality German training 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": "💠 Specialized function calling datasets", "raw": "💠 Specialized function calling datasets", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💠 Premium multilingual content", "raw": "💠 Premium multilingual 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": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🎁 Community Contribution:", "raw": "🎁 Community 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 }, { "type": "text", "value": "We're also releasing two new datasets in a few days: ", "raw": "We're also releasing two new datasets in a few days: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 SauerkrautLM-Fermented-GER-DPO: 3,300 high-quality German training samples", "raw": "1️⃣ SauerkrautLM-Fermented-GER-DPO: 3,300 high-quality German training samples", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 SauerkrautLM-Fermented-Irrelevance-GER-DPO: 2,000 specialized samples for optimized function call irrelevance handling", "raw": "2️⃣ SauerkrautLM-Fermented-Irrelevance-GER-DPO: 2,000 specialized samples for optimized function call irrelevance handling", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Thank you to our incredible community and partners who have supported us throughout this journey. Here's to another year of AI innovation! 🚀", "raw": "Thank you to our incredible community and partners who have supported us throughout this journey. Here's to another year of AI innovation! 🚀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Early Morning Before Work Project:", "raw": "Early Morning Before Work Project:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🌌 Introducing Cascade of Semantically Integrated Layers (CaSIL): A Humorously Over-Engineered Algorithm That Actually… Works 🤷‍♂️", "raw": "🌌 Introducing Cascade of Semantically Integrated Layers (CaSIL): A Humorously Over-Engineered Algorithm That Actually… Works 🤷‍♂️", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 introduce CaSIL – the Cascade of Semantically Integrated Layers. Imagine giving a single question the level of introspection typically reserved for philosophical debates or maybe therapy. In short, CaSIL is a pure Python reasoning algorithm that, in a series of semantically rich layers, takes any input and rebuilds it into a nuanced response that’s (surprisingly) meaningful to a human.", "raw": "Let me introduce CaSIL – the Cascade of Semantically Integrated Layers. Imagine giving a single question the level of introspection typically reserved for philosophical debates or maybe therapy. In short, CaSIL is a pure Python reasoning algorithm that, in a series of semantically rich layers, takes any input and rebuilds it into a nuanced response that’s (surprisingly) meaningful to a human.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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’ve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. It’s built without agent frameworks—just good ol' Python and math—and it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Here’s a quick peek at the steps:", "raw": "I’ve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. It’s built without agent frameworks—just good ol' Python and math—and it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Here’s a quick peek at the steps:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 CaSIL Works:", "raw": "✨ How CaSIL Works:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Initial Understanding: The first layer captures the basic concepts in your input, just as a warm-up.", "raw": "Initial Understanding: The first layer captures the basic concepts in your input, just as a warm-up.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Relationship Analysis: A lightweight knowledge graph (because why not?) maps out related ideas and builds interconnections.", "raw": "Relationship Analysis: A lightweight knowledge graph (because why not?) maps out related ideas and builds interconnections.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Context Integration: Adds historical or contextual knowledge, bringing a bit of depth and relevance.", "raw": "Context Integration: Adds historical or contextual knowledge, bringing a bit of depth and relevance.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Response Synthesis: Pieces it all together, aiming to produce a response that feels more like a conversation than an outdated search result.", "raw": "Response Synthesis: Pieces it all together, aiming to produce a response that feels more like a conversation than an outdated search result.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Does it work? Yes! And in record time, too. Admittedly, the code is rough—two days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; it’s a pure algorithm without complex dependencies, making it easy to integrate into your own LLM setups.", "raw": "Does it work? Yes! And in record time, too. Admittedly, the code is rough—two days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; it’s a pure algorithm without complex dependencies, making it easy to integrate into your own LLM setups.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🔗 Explore the repo here: ", "raw": "🔗 Explore the repo here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers", "href": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📜 Example outputs: ", "raw": "📜 Example outputs: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers/blob/main/examples.md", "href": "https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers/blob/main/examples.md", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "I just shipped ", "raw": "I just shipped ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`retrain-pipelines 0.1.1`", "href": null, "resource": null, "url": null, "code": "retrain-pipelines 0.1.1", "user": null, "label": null, "lang": null }, { "type": "text", "value": " today. The doc is also pimped compared to previous release. That was clearly not mature then.", "raw": " today. The doc is also pimped compared to previous release. That was clearly not mature then.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 have to focus on another project for the next couple weeks but, anyone feel free to open issues on the GitHub repo and discuss any interest you'd have there if you will (please?) !", "raw": "I'll have to focus on another project for the next couple weeks but, anyone feel free to open issues on the GitHub repo and discuss any interest you'd have there if you will (please?) !", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 the meantime, you may enjoy retrying this :", "raw": "In the meantime, you may enjoy retrying 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": "link", "value": null, "raw": "https://huggingface.co/blog/Aurelien-Morgan/stateful-metaflow-on-colab", "href": "https://huggingface.co/blog/Aurelien-Morgan/stateful-metaflow-on-colab", "resource": null, "url": null, "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": "periodic reminder : if you are experiencing ⚠️500 errors ⚠️ or ⚠️ abnormal ", "raw": "periodic reminder : if you are experiencing ⚠️500 errors ⚠️ or ⚠️ abnormal ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`spaces`", "href": null, "resource": null, "url": null, "code": "spaces", "user": null, "label": null, "lang": null }, { "type": "text", "value": " behavior on load or launch ⚠️", "raw": " behavior on load or launch ⚠️", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 a thread 👉🏻 ", "raw": "we have a thread 👉🏻 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://discord.com/channels/879548962464493619/1295847667515129877", "href": "https://discord.com/channels/879548962464493619/1295847667515129877", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 can record the problem and share it there , or on the forums in your own post , please dont be shy because i'm not sure but i do think it helps 🤗🤗🤗", "raw": "if you can record the problem and share it there , or on the forums in your own post , please dont be shy because i'm not sure but i do think it helps 🤗🤗🤗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Vector Search (most) datasets on the Hugging Face Hub 🔦", "raw": "Vector Search (most) datasets on the Hugging Face Hub 🔦", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Powered by: Polars, DuckDB, Gradio and model2vec (lightning-fast embeddings by Stéphan Tulkens).", "raw": "Powered by: Polars, DuckDB, Gradio and model2vec (lightning-fast embeddings by Stéphan Tulkens).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Should work fast enough for datasets up to 100K.", "raw": "Should work fast enough for datasets up to 100K.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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/davidberenstein1957/vectorsearch-hub-datasets", "href": null, "resource": { "type": "space", "id": "davidberenstein1957/vectorsearch-hub-datasets", "discussionNum": null }, "url": "https://huggingface.co/spaces/davidberenstein1957/vectorsearch-hub-datasets", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Hello researchers! Here are scripts to generate reviews on HF Daily Papers:", "raw": "Hello researchers! Here are scripts to generate reviews on HF Daily Papers:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/averkij/top_papers", "href": "https://github.com/averkij/top_papers", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "⚙️ Works on GitHub Actions", "raw": "⚙️ Works on GitHub Actions", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🤖 Claude, GPT-4o, FLUX", "raw": "🤖 Claude, GPT-4o, 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": "text", "value": "🌏 Multiple languages", "raw": "🌏 Multiple languages", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 38 topics (#agents, #multimodal, #plp, etc.)", "raw": "📚 Classification by 38 topics (#agents, #multimodal, #plp, etc.)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 } ]

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[ { "type": "text", "value": "It's work like this that in some way signal the eventual “dominance” of AI over all the sciences.", "raw": "It's work like this that in some way signal the eventual “dominance” of AI over all the sciences.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the model’s displacement outputs”", "raw": "“We train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the model’s displacement 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": "The emulator is capable of predicting", "raw": "The emulator is capable of predicting", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 nonlinear displacement and velocity fields for 128^3 particles in half a second on a single GPU🤯", "raw": "the nonlinear displacement and velocity fields for 128^3 particles in half a second on a single GPU🤯", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Hey, I just added three useful advanced use cases to ", "raw": "Hey, I just added three useful advanced use cases to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/do-me/SemanticFinder/blob/main/README.md#advanced-use-cases", "href": null, "resource": { "type": "dataset", "id": "do-me/SemanticFinder", "discussionNum": null }, "url": "https://huggingface.co/datasets/do-me/SemanticFinder/blob/main/README.md#advanced-use-cases", "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": "SemanticFinder is a collection of embeddings for public documents or books. You can create your own index file from any text or pdf and save it without installing or downloading anything. Try yourself: ", "raw": "SemanticFinder is a collection of embeddings for public documents or books. You can create your own index file from any text or pdf and save it without installing or downloading anything. Try yourself: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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. Translating from 100+ languages to English (even though it might confuse a strawberry with a grapefruit ;D): ", "raw": "1. Translating from 100+ languages to English (even though it might confuse a strawberry with a grapefruit ;D): ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://do-me.github.io/SemanticFinder/?hf=List_of_the_Most_Common_English_Words_70320cde&firstOnly=true&inferencingActive=False", "href": "https://do-me.github.io/SemanticFinder/?hf=List_of_the_Most_Common_English_Words_70320cde&firstOnly=true&inferencingActive=False", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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. Finding English synonyms: ", "raw": "2. Finding English synonyms: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://do-me.github.io/SemanticFinder/?hf=List_of_the_Most_Common_English_Words_0d1e28dc&firstOnly=true&inferencingActive=False", "href": "https://do-me.github.io/SemanticFinder/?hf=List_of_the_Most_Common_English_Words_0d1e28dc&firstOnly=true&inferencingActive=False", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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. The \"universal index idea\": create an embedding index with 30k English words and reuse it on unseen texts. You can decide to fill the gaps in the index by additional inferencing or just stick to the 30k index for instant semantic similarity. ", "raw": "3. The \"universal index idea\": create an embedding index with 30k English words and reuse it on unseen texts. You can decide to fill the gaps in the index by additional inferencing or just stick to the 30k index for instant semantic similarity. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Initial idea: ", "raw": "Initial idea: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/do-me/SemanticFinder/discussions/48", "href": "https://github.com/do-me/SemanticFinder/discussions/48", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 here: ", "raw": "Try here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://do-me.github.io/SemanticFinder/?hf=List_of_the_Most_Common_English_Words_0d1e28dc&inferencingActive=False&universalIndexSettingsWordLevel", "href": "https://do-me.github.io/SemanticFinder/?hf=List_of_the_Most_Common_English_Words_0d1e28dc&inferencingActive=False&universalIndexSettingsWordLevel", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " with a text of your choice.", "raw": " with a text of your choice.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 could be enhanced by adding duplets or triplets like \"climate change\" or \"green house gas\". Eventually I'd like to set up vector DB integrations. ", "raw": "This could be enhanced by adding duplets or triplets like \"climate change\" or \"green house gas\". Eventually I'd like to set up vector DB integrations. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 happy to hear your feedback, ideas and maybe even contributions! :)", "raw": "Super happy to hear your feedback, ideas and maybe even contributions! :)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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": "Edit: Apparently markdown url formatting does only work for HF links.", "raw": "Edit: Apparently markdown url formatting does only work for HF links.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "☁️☔ New Research Alert! ❄️🌙", "raw": "☁️☔ New Research Alert! ❄️🌙", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Title: CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning", "raw": "📄 Title: CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt 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": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📝 Description: CoDA is a UDA methodology that boosts models to understand all adverse scenes (☁️,☔,❄️,🌙) by highlighting the discrepancies within these scenes. CoDA achieves state-of-the-art performances on widely used benchmarks.", "raw": "📝 Description: CoDA is a UDA methodology that boosts models to understand all adverse scenes (☁️,☔,❄️,🌙) by highlighting the discrepancies within these scenes. CoDA achieves state-of-the-art performances on widely used 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": "👥 Authors: Ziyang Gong, Fuhao Li, Yupeng Deng, Deblina Bhattacharjee, Xiangwei Zhu, Zhenming Ji", "raw": "👥 Authors: Ziyang Gong, Fuhao Li, Yupeng Deng, Deblina Bhattacharjee, Xiangwei Zhu, Zhenming Ji", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/2403.17369", "href": null, "resource": { "type": "paper", "id": "2403.17369", "discussionNum": null }, "url": "https://huggingface.co/papers/2403.17369", "code": null, "user": null, "label": "CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual\n Prompt Tuning (2403.17369)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📁 Repository: ", "raw": "📁 Repository: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/Cuzyoung/CoDA", "href": "https://github.com/Cuzyoung/CoDA", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Papers: more cutting-edge research presented at other conferences in the ", "raw": "📚 More Papers: more cutting-edge research presented at other conferences in the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "href": null, "resource": { "type": "space", "id": "DmitryRyumin/NewEraAI-Papers", "discussionNum": null }, "url": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " curated by ", "raw": " curated by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@DmitryRyumin", "href": null, "resource": null, "url": null, "code": null, "user": "DmitryRyumin", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🔍 Keywords: #CoDA #DomainAdaptation #VisualPromptTuning #SAVPT #DeepLearning #Innovation", "raw": "🔍 Keywords: #CoDA #DomainAdaptation #VisualPromptTuning #SAVPT #DeepLearning #Innovation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Are you looking for the perfect leaderboard/arena for your use case? 👀", "raw": "Are you looking for the perfect leaderboard/arena for your use case? 👀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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's a new tool for this! ", "raw": "There's a new tool for 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": "link", "value": null, "raw": "https://huggingface.co/spaces/leaderboards/LeaderboardFinder", "href": "https://huggingface.co/spaces/leaderboards/LeaderboardFinder", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Select your modality, language, task... then search! 🔍 ", "raw": "Select your modality, language, task... then search! 🔍 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Some categories of interest: ", "raw": "Some categories of interest: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- does the leaderboard accept submissions?", "raw": "- does the leaderboard accept submissions?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- is the test set private or public?", "raw": "- is the test set private or public?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- is it using an automatic metric, human evaluators, or llm as a judge? ", "raw": "- is it using an automatic metric, human evaluators, or llm as a judge? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 spaces list is build from space metadata, and reloaded every hour.", "raw": "The spaces list is build from space metadata, and reloaded every hour.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Enjoy!", "raw": "Enjoy!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Excited to introduce Jamba by AI21", "raw": "Excited to introduce Jamba by AI21", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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/ai21labs/Jamba-v0.1", "href": null, "resource": { "type": "model", "id": "ai21labs/Jamba-v0.1", "discussionNum": null }, "url": "https://huggingface.co/ai21labs/Jamba-v0.1", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 are thrilled to announce Jamba, the world’s first production-grade Mamba based model. ", "raw": "We are thrilled to announce Jamba, the world’s first production-grade Mamba based 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": "Key Features: ", "raw": "Key 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": "text", "value": "- First production-grade Mamba based model built on a novel SSM-Transformer hybrid architecture", "raw": "- First production-grade Mamba based model built on a novel SSM-Transformer hybrid 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": "- 3X throughput on long contexts compared to Mixtral 8x7B", "raw": "- 3X throughput on long contexts compared to Mixtral 8x7B", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Democratizes access to a massive 256K context window", "raw": "- Democratizes access to a massive 256K context window", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 only model in its size class that fits up to 140K context on a single GPU ", "raw": "- The only model in its size class that fits up to 140K context on a single GPU ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Jamba is based on a novel architecture that combines Mamba and Transformer. While our initial results show great efficiency gains, we expect this to be further explored and improved with the help of the community.", "raw": "Jamba is based on a novel architecture that combines Mamba and Transformer. While our initial results show great efficiency gains, we expect this to be further explored and improved with the help of the 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": "Check out our blog post for more info: ", "raw": "Check out our blog post for more info: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://ai21-labs.webflow.io/blog/announcing-jamba", "href": "https://ai21-labs.webflow.io/blog/announcing-jamba", "resource": null, "url": null, "code": null, "user": null, "label": 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": "A new paper titled \"Long-Form Factuality in Large Language Models\" proposes a new approach to evaluate the long-form factuality of large language models using an AI agent! They introduce SAFE (Search-Augmented Factuality Evaluator) which leverages an LLM to break down responses into individual facts, query Google to verify each fact, and perform multi-step reasoning.", "raw": "A new paper titled \"Long-Form Factuality in Large Language Models\" proposes a new approach to evaluate the long-form factuality of large language models using an AI agent! They introduce SAFE (Search-Augmented Factuality Evaluator) which leverages an LLM to break down responses into individual facts, query Google to verify each fact, and perform multi-step 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": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Keypoints:", "raw": "Keypoints:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* SAFE (Search-Augmented Factuality Evaluator) is an automated method using an LLM agent to evaluate factuality", "raw": "* SAFE (Search-Augmented Factuality Evaluator) is an automated method using an LLM agent to evaluate factuality", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 also introduces LongFact, a 2,280 prompt set spanning 38 topics to test open-domain factual knowledge", "raw": "* It also introduces LongFact, a 2,280 prompt set spanning 38 topics to test open-domain factual 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": "text", "value": "* SAFE achieves a 72% humans agreement while being 20x cheaper. It also wins 76% of the disagreements measured on a small scale experiment where a more thorough human procedure (researchers + full internet search) was used.", "raw": "* SAFE achieves a 72% humans agreement while being 20x cheaper. It also wins 76% of the disagreements measured on a small scale experiment where a more thorough human procedure (researchers + full internet search) was 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": "text", "value": "* Larger models like GPT-4, Claude Opus and Gemini Ultra tend to exhibit better long-form factuality.", "raw": "* Larger models like GPT-4, Claude Opus and Gemini Ultra tend to exhibit better long-form factuality.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/2403.18802", "href": null, "resource": { "type": "paper", "id": "2403.18802", "discussionNum": null }, "url": "https://huggingface.co/papers/2403.18802", "code": null, "user": null, "label": "Long-form factuality in large language models (2403.18802)", "lang": null }, { "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 and data: ", "raw": "Code and data: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/google-deepmind/long-form-factuality", "href": "https://github.com/google-deepmind/long-form-factuality", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 authors for their work!", "raw": "Congrats to the authors for their work!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "A Little guide to building Large Language Models in 2024", "raw": "A Little guide to building Large Language Models in 2024", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 post-recording of a 75min lecture I gave two weeks ago on how to train a LLM from scratch in 2024. I tried to keep it short and comprehensive – focusing on concepts that are crucial for training good LLM but often hidden in tech reports.", "raw": "This is a post-recording of a 75min lecture I gave two weeks ago on how to train a LLM from scratch in 2024. I tried to keep it short and comprehensive – focusing on concepts that are crucial for training good LLM but often hidden in tech reports.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 the lecture, I introduce the students to all the important concepts/tools/techniques for training good performance LLM:", "raw": "In the lecture, I introduce the students to all the important concepts/tools/techniques for training good performance 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": "* finding, preparing and evaluating web scale data", "raw": "* finding, preparing and evaluating web scale 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": "* understanding model parallelism and efficient training", "raw": "* understanding model parallelism and efficient 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": "* fine-tuning/aligning models", "raw": "* fine-tuning/aligning 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": "text", "value": "* fast inference", "raw": "* fast inference", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 is of course many things and details missing and that I should have added to it, don't hesitate to tell me you're most frustrating omission and I'll add it in a future part. In particular I think I'll add more focus on how to filter topics well and extensively and maybe more practical anecdotes and details.", "raw": "There is of course many things and details missing and that I should have added to it, don't hesitate to tell me you're most frustrating omission and I'll add it in a future part. In particular I think I'll add more focus on how to filter topics well and extensively and maybe more practical anecdotes and 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": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Now that I recorded it I've been thinking this could be part 1 of a two-parts series with a 2nd fully hands-on video on how to run all these steps with some libraries and recipes we've released recently at HF around LLM training (and could be easily adapted to your other framework anyway):", "raw": "Now that I recorded it I've been thinking this could be part 1 of a two-parts series with a 2nd fully hands-on video on how to run all these steps with some libraries and recipes we've released recently at HF around LLM training (and could be easily adapted to your other framework anyway):", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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": "`datatrove`", "href": null, "resource": null, "url": null, "code": "datatrove", "user": null, "label": null, "lang": null }, { "type": "text", "value": " for all things web-scale data preparation: ", "raw": " for all things web-scale data preparation: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/huggingface/datatrove", "href": "https://github.com/huggingface/datatrove", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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": "`nanotron`", "href": null, "resource": null, "url": null, "code": "nanotron", "user": null, "label": null, "lang": null }, { "type": "text", "value": " for lightweight 4D parallelism LLM training: ", "raw": " for lightweight 4D parallelism LLM training: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/huggingface/nanotron", "href": "https://github.com/huggingface/nanotron", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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": "`lighteval`", "href": null, "resource": null, "url": null, "code": "lighteval", "user": null, "label": null, "lang": null }, { "type": "text", "value": " for in-training fast parallel LLM evaluations: ", "raw": " for in-training fast parallel LLM evaluations: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/huggingface/lighteval", "href": "https://github.com/huggingface/lighteval", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 link to watch the lecture on Youtube: ", "raw": "Here is the link to watch the lecture on Youtube: ", "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=2-SPH9hIKT8", "href": "https://www.youtube.com/watch?v=2-SPH9hIKT8", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 link to the Google slides: ", "raw": "And here is the link to the Google slides: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://docs.google.com/presentation/d/1IkzESdOwdmwvPxIELYJi8--K3EZ98_cL6c5ZcLKSyVg/edit#slide=id.p", "href": "https://docs.google.com/presentation/d/1IkzESdOwdmwvPxIELYJi8--K3EZ98_cL6c5ZcLKSyVg/edit#slide=id.p", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Enjoy and happy to hear feedback on it and what to add, correct, extend in a second part.", "raw": "Enjoy and happy to hear feedback on it and what to add, correct, extend in a second part.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "𝐓𝐡𝐞 𝐫𝐞𝐭𝐮𝐫𝐧 𝐨𝐟 𝐭𝐡𝐞 𝐑𝐍𝐍𝐬 ⚔ 𝐍𝐞𝐰 𝐌𝐚𝐦𝐛𝐚-𝐛𝐚𝐬𝐞𝐝 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 \"𝐉𝐚𝐦𝐛𝐚\"", "raw": "𝐓𝐡𝐞 𝐫𝐞𝐭𝐮𝐫𝐧 𝐨𝐟 𝐭𝐡𝐞 𝐑𝐍𝐍𝐬 ⚔ 𝐍𝐞𝐰 𝐌𝐚𝐦𝐛𝐚-𝐛𝐚𝐬𝐞𝐝 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 \"𝐉𝐚𝐦𝐛𝐚\"", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Since the release of BERT by Google in 2019, Transformers architecture have taken over machine learning thanks to their 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺, that gives them the ability to focus on important points of the input. But 𝙖𝙩𝙩𝙚𝙣𝙩𝙞𝙤𝙣 𝙘𝙤𝙢𝙥𝙪𝙩𝙖𝙩𝙞𝙤𝙣 𝙞𝙨 𝙦𝙪𝙖𝙙𝙧𝙖𝙩𝙞𝙘 𝙞𝙣 𝙩𝙝𝙚 𝙞𝙣𝙥𝙪𝙩 𝙡𝙚𝙣𝙜𝙩𝙝.", "raw": "Since the release of BERT by Google in 2019, Transformers architecture have taken over machine learning thanks to their 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺, that gives them the ability to focus on important points of the input. But 𝙖𝙩𝙩𝙚𝙣𝙩𝙞𝙤𝙣 𝙘𝙤𝙢𝙥𝙪𝙩𝙖𝙩𝙞𝙤𝙣 𝙞𝙨 𝙦𝙪𝙖𝙙𝙧𝙖𝙩𝙞𝙘 𝙞𝙣 𝙩𝙝𝙚 𝙞𝙣𝙥𝙪𝙩 𝙡𝙚𝙣𝙜𝙩𝙝.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Mamba paper, published in December 2023, announced the return of the RNNs: it has no attention, but integrates a selection mechanism, which should be able to reproduce the “focus” ability of attention, in an architecture for which the compute requirements 𝗴𝗿𝗼𝘄 𝗼𝗻𝗹𝘆 𝗹𝗶𝗻𝗲𝗮𝗿𝗹𝘆 𝗶𝗻 𝗶𝗻𝗽𝘂𝘁 𝗹𝗲𝗻𝗴𝘁𝗵!", "raw": "💫 The Mamba paper, published in December 2023, announced the return of the RNNs: it has no attention, but integrates a selection mechanism, which should be able to reproduce the “focus” ability of attention, in an architecture for which the compute requirements 𝗴𝗿𝗼𝘄 𝗼𝗻𝗹𝘆 𝗹𝗶𝗻𝗲𝗮𝗿𝗹𝘆 𝗶𝗻 𝗶𝗻𝗽𝘂𝘁 𝗹𝗲𝗻𝗴𝘁𝗵!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 this work? We had yet to see a large Mamba model recovering the performance of Attention-based Transformers.", "raw": "🤔 Would this work? We had yet to see a large Mamba model recovering the performance of Attention-based 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": "💥 But now it's done! A (Mamba + Transformers) hybrid just beat Transformers!", "raw": "💥 But now it's done! A (Mamba + Transformers) hybrid just beat 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": "The AI21 Labs team just released Jamba.", "raw": "The AI21 Labs team just released Jamba.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 insert a few Transformer layers to inject some attention in a big pile of Mamba layers, thus getting the best of both worlds.", "raw": "They insert a few Transformer layers to inject some attention in a big pile of Mamba layers, thus getting the best of both worlds.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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:", "raw": "𝙏𝙇;𝘿𝙍:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 𝗠𝗼𝗘 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: 4 Jamba blocks, each of these being 7 Mamba layers for 1 Transformer.", "raw": "🏗️ 𝗡𝗲𝘄 𝗠𝗼𝗘 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: 4 Jamba blocks, each of these being 7 Mamba layers for 1 Transformer.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🏋️ 𝟱𝟮𝗕 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝘀, 𝟭𝟮𝗕 𝗮𝗰𝘁𝗶𝘃𝗲 𝗮𝘁 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲: This reduction is enabled by Mixture of Experts, and similar to Mixtral (47B parameters - 13B active).", "raw": "🏋️ 𝟱𝟮𝗕 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝘀, 𝟭𝟮𝗕 𝗮𝗰𝘁𝗶𝘃𝗲 𝗮𝘁 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲: This reduction is enabled by Mixture of Experts, and similar to Mixtral (47B parameters - 13B active).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🏎️ 𝗦𝗽𝗲𝗲𝗱: 𝘅𝟯 𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁. Jamba is much faster than similar-sized Transformer models on long contexts.", "raw": "🏎️ 𝗦𝗽𝗲𝗲𝗱: 𝘅𝟯 𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁. Jamba is much faster than similar-sized Transformer models on long contexts.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📏 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗹𝗲𝗻𝗴𝘁𝗵: 𝟭𝟰𝟬𝗞 𝘁𝗼𝗸𝗲𝗻𝘀 on a single 80GB A100!", "raw": "📏 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗹𝗲𝗻𝗴𝘁𝗵: 𝟭𝟰𝟬𝗞 𝘁𝗼𝗸𝗲𝗻𝘀 on a single 80GB A100!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💪 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: 𝘀𝘁𝗮𝘁𝗲-𝗼𝗳-𝘁𝗵𝗲-𝗮𝗿𝘁 𝗳𝗼𝗿 𝘁𝗵𝗶𝘀 𝘀𝗶𝘇𝗲. The small injection of attention seems sufficient since Jamba beats the open-source reference Mixtral-8x7B on many benchmarks!", "raw": "💪 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: 𝘀𝘁𝗮𝘁𝗲-𝗼𝗳-𝘁𝗵𝗲-𝗮𝗿𝘁 𝗳𝗼𝗿 𝘁𝗵𝗶𝘀 𝘀𝗶𝘇𝗲. The small injection of attention seems sufficient since Jamba beats the open-source reference Mixtral-8x7B on many 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": "Try it here 👉 ", "raw": "Try it here 👉 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ai21labs/Jamba-v0.1", "href": null, "resource": { "type": "model", "id": "ai21labs/Jamba-v0.1", "discussionNum": null }, "url": "https://huggingface.co/ai21labs/Jamba-v0.1", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Just now, we release a small MoE model, Qwen1.5-MoE-A2.7B, a 14B model with 2.7B activated parameters. Leaving the hype, I would love to share more things here in HF. But if you don't know much about this, check our blog for more info: ", "raw": "Just now, we release a small MoE model, Qwen1.5-MoE-A2.7B, a 14B model with 2.7B activated parameters. Leaving the hype, I would love to share more things here in HF. But if you don't know much about this, check our blog for more info: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://qwenlm.github.io/blog/qwen-moe/", "href": "https://qwenlm.github.io/blog/qwen-moe/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "At the beginning, it was trying with the MoE stuff, making Megatron work well with MegaBlocks. As always, we worked with small ones first. However, we have been struggling with a lot of details.", "raw": "At the beginning, it was trying with the MoE stuff, making Megatron work well with MegaBlocks. As always, we worked with small ones first. However, we have been struggling with a lot of 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": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "With megablocks and so many tricks that make training MoE models work, it is almost impossible to fail. The challenge is actually how good your model is. Then things became more complex than I had expected. Finegrained experts actually pissed me off but damn it works for the model at this scale. However, it brings complexity to the model, and this is somehow why at this moment our codes are not merged into llama.cpp cuz it really brings problems. Shared experts might be good, but we need more engineering efforts to really unleash its benefits in inference acceleration. ", "raw": "With megablocks and so many tricks that make training MoE models work, it is almost impossible to fail. The challenge is actually how good your model is. Then things became more complex than I had expected. Finegrained experts actually pissed me off but damn it works for the model at this scale. However, it brings complexity to the model, and this is somehow why at this moment our codes are not merged into llama.cpp cuz it really brings problems. Shared experts might be good, but we need more engineering efforts to really unleash its benefits in inference acceleration. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 community, this is actually our first time releasing an MoE model. We don't know what will happen to us, but we are prepared for complaints. I just hope that we can really make things clear, and provide a good recipe to play with our MoE model just like people playing with Mixtral.", "raw": "For the community, this is actually our first time releasing an MoE model. We don't know what will happen to us, but we are prepared for complaints. I just hope that we can really make things clear, and provide a good recipe to play with our MoE model just like people playing with Mixtral.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": " 🎈 LLM Benchmarks Update!", "raw": " 🎈 LLM Benchmarks 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": "**tl;dr: do not depend on benchmark leaderboards to choose your \"chatbot\" model! (Especially for non-English languages.)**", "raw": "**tl;dr: do not depend on benchmark leaderboards to choose your \"chatbot\" model! (Especially for non-English languages.)**", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 of all, I'm discontinuing the Open #Dutch #LLM Leaderboard (", "raw": "First of all, I'm discontinuing the Open #Dutch #LLM Leaderboard (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://lnkd.in/eFnsaFR6", "href": "https://lnkd.in/eFnsaFR6", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "). It will stay online for now, but I urge the use of the ScandEval leaderboard instead (", "raw": "). It will stay online for now, but I urge the use of the ScandEval leaderboard instead (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://scandeval.com/dutch-nlg/", "href": "https://scandeval.com/dutch-nlg/", "resource": null, "url": null, "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": "mention", "value": null, "raw": "@saattrupdan", "href": null, "resource": null, "url": null, "code": null, "user": "saattrupdan", "label": null, "lang": null }, { "type": "text", "value": ". It contains more tasks, has better reproducibility and statistics (CI) and a flexible back-end library (", "raw": ". It contains more tasks, has better reproducibility and statistics (CI) and a flexible back-end library (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`scandeval`", "href": null, "resource": null, "url": null, "code": "scandeval", "user": null, "label": null, "lang": null }, { "type": "text", "value": ") to run your own benchmarks with. As part of project \"Leesplank\" (with Michiel Buisman and Maarten Lens-FitzGerald) we recently added GPT-4-1106-preview scores to add a good \"target\" to the leaderboard.", "raw": ") to run your own benchmarks with. As part of project \"Leesplank\" (with Michiel Buisman and Maarten Lens-FitzGerald) we recently added GPT-4-1106-preview scores to add a good \"target\" to the 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": "An important note here is that benchmark leaderboards are not a golden truth. Especially evaluating generative models is hard. You run into issues like prompt engineering (and sensitivity of models to one or other prompt), structured output generation, and - quite simply - \"how to automatically evaluate open-ended generation\".", "raw": "An important note here is that benchmark leaderboards are not a golden truth. Especially evaluating generative models is hard. You run into issues like prompt engineering (and sensitivity of models to one or other prompt), structured output generation, and - quite simply - \"how to automatically evaluate open-ended 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": "💡 Another important but under-discussed facet is the discrepancy between models' capability of understanding vs. generating *in different languages* (so the NLU part of NLG benchmarking). In other words: some of the listed models score really well on, e.g., MCQ benchmarks but are not suitable to use as DUTCH chat bots. Interestingly, some of these models seem to understand questions in Dutch and are able to pick the right answer (because they have good knowledge or reasoning skills), but generating fluent and grammatical Dutch is something else entirely! This is perhaps also true for humans: it's easier to sort-of grasp the meaning of a new language and answer with \"Yes\" or \"No\", but answering fluently in the language is much harder! Yet, your language production fluency does not necessarily say anything about your knowledge and reasoning skills.", "raw": "💡 Another important but under-discussed facet is the discrepancy between models' capability of understanding vs. generating *in different languages* (so the NLU part of NLG benchmarking). In other words: some of the listed models score really well on, e.g., MCQ benchmarks but are not suitable to use as DUTCH chat bots. Interestingly, some of these models seem to understand questions in Dutch and are able to pick the right answer (because they have good knowledge or reasoning skills), but generating fluent and grammatical Dutch is something else entirely! This is perhaps also true for humans: it's easier to sort-of grasp the meaning of a new language and answer with \"Yes\" or \"No\", but answering fluently in the language is much harder! Yet, your language production fluency does not necessarily say anything about your knowledge and reasoning skills.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Hopefully we can get a chat arena for Dutch some day - user feedback is the most powerful metric!", "raw": "Hopefully we can get a chat arena for Dutch some day - user feedback is the most powerful metric!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": 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": "Compared Effect Of Image Captioning For SDXL Fine-tuning / DreamBooth Training for a Single Person, 10.3 GB VRAM via OneTrainer", "raw": "Compared Effect Of Image Captioning For SDXL Fine-tuning / DreamBooth Training for a Single Person, 10.3 GB VRAM via OneTrainer", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Sadly the post character count is limited so please read full info on Medium here", "raw": "Sadly the post character count is limited so please read full info on Medium 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": "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://medium.com/@furkangozukara/compared-effect-of-image-captioning-for-sdxl-fine-tuning-dreambooth-training-for-a-single-person-961087e42334", "href": "https://medium.com/@furkangozukara/compared-effect-of-image-captioning-for-sdxl-fine-tuning-dreambooth-training-for-a-single-person-961087e42334", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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": "🚀🎭🌟 New Research Alert! 🌟 🎭🚀", "raw": "🚀🎭🌟 New Research Alert! 🌟 🎭🚀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Title: AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation 🔝", "raw": "📄 Title: AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation 🔝", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📝 Description: AniPortrait is a novel framework for generating photorealistic portrait animations driven by audio and a reference image, with superior facial naturalness, pose variety, and visual quality, with potential applications in facial motion editing and facial reenactment.", "raw": "📝 Description: AniPortrait is a novel framework for generating photorealistic portrait animations driven by audio and a reference image, with superior facial naturalness, pose variety, and visual quality, with potential applications in facial motion editing and facial reenactment.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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: Huawei Wei, ", "raw": "👥 Authors: Huawei Wei, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@ZJYang", "href": null, "resource": null, "url": null, "code": null, "user": "ZJYang", "label": null, "lang": null }, { "type": "text", "value": ", Zhisheng Wang", "raw": ", Zhisheng Wang", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/2403.17694", "href": null, "resource": { "type": "paper", "id": "2403.17694", "discussionNum": null }, "url": "https://huggingface.co/papers/2403.17694", "code": null, "user": null, "label": "AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation (2403.17694)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📁 Repository: ", "raw": "📁 Repository: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/Zejun-Yang/AniPortrait", "href": "https://github.com/Zejun-Yang/AniPortrait", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/ZJYang/AniPortrait_official", "href": null, "resource": { "type": "space", "id": "ZJYang/AniPortrait_official", "discussionNum": null }, "url": "https://huggingface.co/spaces/ZJYang/AniPortrait_official", "code": null, "user": null, "label": null, "lang": null }, { "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/ZJYang/AniPortrait", "href": null, "resource": { "type": "model", "id": "ZJYang/AniPortrait", "discussionNum": null }, "url": "https://huggingface.co/ZJYang/AniPortrait", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Papers: more cutting-edge research presented at other conferences in the ", "raw": "📚 More Papers: more cutting-edge research presented at other conferences in the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "href": null, "resource": { "type": "space", "id": "DmitryRyumin/NewEraAI-Papers", "discussionNum": null }, "url": "https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " curated by ", "raw": " curated by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@DmitryRyumin", "href": null, "resource": null, "url": null, "code": null, "user": "DmitryRyumin", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": 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null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🔍 Keywords: #AniPortrait #Animation #AudioDriven #Photorealistic #FacialAnimation #DeepLearning #Innovation", "raw": "🔍 Keywords: #AniPortrait #Animation #AudioDriven #Photorealistic #FacialAnimation #DeepLearning #Innovation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "The Unreasonable Ineffectiveness of the Deeper Layers", "raw": "The Unreasonable Ineffectiveness of the Deeper Layers", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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/2403.17887", "href": null, "resource": { "type": "paper", "id": "2403.17887", "discussionNum": null }, "url": "https://huggingface.co/papers/2403.17887", "code": null, "user": null, "label": "The Unreasonable Ineffectiveness of the Deeper Layers (2403.17887)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 empirically study a simple layer-pruning strategy for popular families of open-weight pretrained LLMs, finding minimal degradation of performance on different question-answering benchmarks until after a large fraction (up to half) of the layers are removed. To prune these models, we identify the optimal block of layers to prune by considering similarity across layers; then, to \"heal\" the damage, we perform a small amount of finetuning. In particular, we use parameter-efficient finetuning (PEFT) methods, specifically quantization and Low Rank Adapters (QLoRA), such that each of our experiments can be performed on a single A100 GPU. From a practical perspective, these results suggest that layer pruning methods can complement other PEFT strategies to further reduce computational resources of finetuning on the one hand, and can improve the memory and latency of inference on the other hand. From a scientific perspective, the robustness of these LLMs to the deletion of layers implies either that current pretraining methods are not properly leveraging the parameters in the deeper layers of the network or that the shallow layers play a critical role in storing knowledge.", "raw": "We empirically study a simple layer-pruning strategy for popular families of open-weight pretrained LLMs, finding minimal degradation of performance on different question-answering benchmarks until after a large fraction (up to half) of the layers are removed. To prune these models, we identify the optimal block of layers to prune by considering similarity across layers; then, to \"heal\" the damage, we perform a small amount of finetuning. In particular, we use parameter-efficient finetuning (PEFT) methods, specifically quantization and Low Rank Adapters (QLoRA), such that each of our experiments can be performed on a single A100 GPU. From a practical perspective, these results suggest that layer pruning methods can complement other PEFT strategies to further reduce computational resources of finetuning on the one hand, and can improve the memory and latency of inference on the other hand. From a scientific perspective, the robustness of these LLMs to the deletion of layers implies either that current pretraining methods are not properly leveraging the parameters in the deeper layers of the network or that the shallow layers play a critical role in storing knowledge.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "SegGPT is a vision generalist on image segmentation, quite like GPTs for computer vision ✨", "raw": "SegGPT is a vision generalist on image segmentation, quite like GPTs for computer vision ✨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 comes with the last release of transformers 🎁 Demo and more in this post!", "raw": "It comes with the last release of transformers 🎁 Demo and more in this 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": "SegGPT is an extension of the Painter, where you speak to images with images: the model takes in an image prompt, transformed version of the image prompt, the actual image you want to see the same transform, and expected to output the transformed image. ", "raw": "SegGPT is an extension of the Painter, where you speak to images with images: the model takes in an image prompt, transformed version of the image prompt, the actual image you want to see the same transform, and expected to output the transformed 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": "text", "value": "SegGPT consists of a vanilla ViT with a decoder on top (linear, conv, linear).", "raw": "SegGPT consists of a vanilla ViT with a decoder on top (linear, conv, linear).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 model is trained on diverse segmentation examples, where they provide example image-mask pairs, the actual input to be segmented, and the decoder head learns to reconstruct the mask output.", "raw": "The model is trained on diverse segmentation examples, where they provide example image-mask pairs, the actual input to be segmented, and the decoder head learns to reconstruct the mask output.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 generalizes pretty well! ", "raw": "This generalizes pretty 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 }, { "type": "text", "value": "The authors do not claim state-of-the-art results as the model is mainly used zero-shot and few-shot inference. They also do prompt tuning, where they freeze the parameters of the model and only optimize the image tensor (the input context). ", "raw": "The authors do not claim state-of-the-art results as the model is mainly used zero-shot and few-shot inference. They also do prompt tuning, where they freeze the parameters of the model and only optimize the image tensor (the input context). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 🤗 transformers you can use this model easily!", "raw": "Thanks to 🤗 transformers you can use this model easily!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 here ", "raw": "See here ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/docs/transformers/en/model_doc/seggpt", "href": "https://huggingface.co/docs/transformers/en/model_doc/seggpt", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 built an app for you to try it out. I combined SegGPT with Depth Anything Model, so you don't have to upload image mask prompts in your prompt pair 🤗", "raw": "I have built an app for you to try it out. I combined SegGPT with Depth Anything Model, so you don't have to upload image mask prompts in your prompt pair 🤗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 here ", "raw": "Try it here ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/merve/seggpt-depth-anything", "href": null, "resource": { "type": "space", "id": "merve/seggpt-depth-anything", "discussionNum": null }, "url": "https://huggingface.co/spaces/merve/seggpt-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": "text", "value": "Also check out the collection ", "raw": "Also check out 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/merve/seggpt-660466a303bc3cd7559d271b", "href": null, "resource": { "type": "collection", "id": "merve/seggpt-660466a303bc3cd7559d271b", "discussionNum": null }, "url": "https://huggingface.co/collections/merve/seggpt-660466a303bc3cd7559d271b", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Thanks to the 4000+ attempts to jailbreak LLM chatbots and 2000+ votes cast on the Chatbot Guardrails Arena in the 4 days since release! ", "raw": "Thanks to the 4000+ attempts to jailbreak LLM chatbots and 2000+ votes cast on the Chatbot Guardrails Arena in the 4 days since release! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Based on the players' feedback, we have updated the instructions to be clearer and to emphasize that players should vote after identifying a secure chatbot. ", "raw": "Based on the players' feedback, we have updated the instructions to be clearer and to emphasize that players should vote after identifying a secure chatbot. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Tell us what you think when you play again: ", "raw": "Tell us what you think when you play again: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/lighthouzai/guardrails-arena", "href": null, "resource": { "type": "space", "id": "lighthouzai/guardrails-arena", "discussionNum": null }, "url": "https://huggingface.co/spaces/lighthouzai/guardrails-arena", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": 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": "Here, we share the technical details of InternLM2, the current state-of-the-art open-source LLMs!!", "raw": "Here, we share the technical details of InternLM2, the current state-of-the-art open-source 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": "See collections ", "raw": "See collections ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/internlm/internlm2-65b0ce04970888799707893c", "href": null, "resource": { "type": "collection", "id": "internlm/internlm2-65b0ce04970888799707893c", "discussionNum": null }, "url": "https://huggingface.co/collections/internlm/internlm2-65b0ce04970888799707893c", "code": null, "user": null, "label": null, "lang": null }, { "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/2403.17297", "href": null, "resource": { "type": "paper", "id": "2403.17297", "discussionNum": null }, "url": "https://huggingface.co/papers/2403.17297", "code": null, "user": null, "label": "InternLM2 Technical Report (2403.17297)", "lang": null } ]

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[ { "type": "text", "value": "Have we really squeezed out the capacity of a compact chat model? Thrilled to see our latest open model, Starling-7B, ranks 13th among all models in Chatbot Arena! ", "raw": "Have we really squeezed out the capacity of a compact chat model? Thrilled to see our latest open model, Starling-7B, ranks 13th among all models in Chatbot Arena! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 a 7B model, Starling surpasses larger open and proprietary models, including Claude-2, GPT-3.5-Turbo, Gemini Pro, Mixtral 8x7B and Llama2-70B, and is currently the best 7B chat model in Chatbot Arena!", "raw": "🚀 As a 7B model, Starling surpasses larger open and proprietary models, including Claude-2, GPT-3.5-Turbo, Gemini Pro, Mixtral 8x7B and Llama2-70B, and is currently the best 7B chat model in Chatbot Arena!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 out the model on HF here: ", "raw": "Try out the model on HF here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Nexusflow/Starling-LM-7B-beta", "href": null, "resource": { "type": "model", "id": "Nexusflow/Starling-LM-7B-beta", "discussionNum": null }, "url": "https://huggingface.co/Nexusflow/Starling-LM-7B-beta", "code": null, "user": null, "label": 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 state-of-the-art open LLM! 🚀 Databricks just released DBRX, a 132B MoE trained on 12T tokens. Claiming to surpass OpenAI GPT-3.5 and is competitive with Google Gemini 1.0 Pro. 🤯", "raw": "New state-of-the-art open LLM! 🚀 Databricks just released DBRX, a 132B MoE trained on 12T tokens. Claiming to surpass OpenAI GPT-3.5 and is competitive with Google Gemini 1.0 Pro. 🤯", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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", "raw": "TL;DR", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🧮 132B MoE with 16 experts with 4 active in generation", "raw": "🧮 132B MoE with 16 experts with 4 active in 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": "text", "value": "🪟 32 000 context window", "raw": "🪟 32 000 context window", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 open LLMs on common benchmarks, including MMLU", "raw": "📈 Outperforms open LLMs on common benchmarks, including MMLU", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🚀 Up to 2x faster inference than Llama 2 70B", "raw": "🚀 Up to 2x faster inference than Llama 2 70B", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 12T tokens", "raw": "💻 Trained on 12T tokens", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 the GPT-4 tokenizer", "raw": "🔡 Uses the GPT-4 tokenizer", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 License, commercially useable", "raw": "📜 Custom License, commercially useable", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Collection: ", "raw": "Collection: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/databricks/dbrx-6601c0852a0cdd3c59f71962", "href": null, "resource": { "type": "collection", "id": "databricks/dbrx-6601c0852a0cdd3c59f71962", "discussionNum": null }, "url": "https://huggingface.co/collections/databricks/dbrx-6601c0852a0cdd3c59f71962", "code": null, "user": null, "label": null, "lang": null }, { "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/databricks/dbrx-instruct", "href": null, "resource": { "type": "space", "id": "databricks/dbrx-instruct", "discussionNum": null }, "url": "https://huggingface.co/spaces/databricks/dbrx-instruct", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Team at Databricks and MosaicML for this strong release in the open community! 🤗", "raw": "Kudos to the Team at Databricks and MosaicML for this strong release in the open 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 } ]

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[ { "type": "text", "value": " 🐰 Happy ML Easter! 🐰", "raw": " 🐰 Happy ML Easter! 🐰", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 recently had a wager with my colleagues which had me create AI-assisted videos of myself in an Easter Bunny costume singing an AI-generated easter song (in different languages).", "raw": "I recently had a wager with my colleagues which had me create AI-assisted videos of myself in an Easter Bunny costume singing an AI-generated easter song (in different languages).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 did it in 3 simple steps:", "raw": "I did it in 3 simple 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": "🐇I created an easter Bunny version of myself with this space: ", "raw": "🐇I created an easter Bunny version of myself with this space: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/multimodalart/Ip-Adapter-FaceID", "href": null, "resource": { "type": "space", "id": "multimodalart/Ip-Adapter-FaceID", "discussionNum": null }, "url": "https://huggingface.co/spaces/multimodalart/Ip-Adapter-FaceID", "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": "mention", "value": null, "raw": "@multimodalart", "href": null, "resource": null, "url": null, "code": null, "user": "multimodalart", "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": "🎶 Created a song on suno.ai", "raw": "🎶 Created a song on suno.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": "text", "value": "🎞️ Used the Dreamtalk space by ", "raw": "🎞️ Used the Dreamtalk space by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@fffiloni", "href": null, "resource": null, "url": null, "code": null, "user": "fffiloni", "label": null, "lang": null }, { "type": "text", "value": " to animate the image, conditioned on the song: ", "raw": " to animate the image, conditioned on the song: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/fffiloni/dreamtalk", "href": null, "resource": { "type": "space", "id": "fffiloni/dreamtalk", "discussionNum": null }, "url": "https://huggingface.co/spaces/fffiloni/dreamtalk", "code": null, "user": null, "label": null, "lang": null } ]

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[]

[ { "type": "text", "value": "Would 1-2 sentence tl;dr summaries of datasets on the Hub be useful for you? ", "raw": "Would 1-2 sentence tl;dr summaries of datasets on the Hub be useful for you? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 example, for the ", "raw": "For example, for the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T", "href": null, "resource": { "type": "dataset", "id": "togethercomputer/RedPajama-Data-1T", "discussionNum": null }, "url": "https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " dataset, would the following summary help give you a quick sense of its content? ", "raw": " dataset, would the following summary help give you a quick sense of its 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": "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: RedPajama is a fully open-source implementation of the LLaMa dataset, consisting of 1.2 trillion tokens from sources like Commoncrawl, C4, GitHub, Books, ArXiv, Wikipedia, and StackExchange, primarily in English, and is structured with metadata for each text sample. ", "raw": "> tl;dr: RedPajama is a fully open-source implementation of the LLaMa dataset, consisting of 1.2 trillion tokens from sources like Commoncrawl, C4, GitHub, Books, ArXiv, Wikipedia, and StackExchange, primarily in English, and is structured with metadata for each text sample. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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've created a dataset with example summaries of the 500 most liked datasets on the Hub: ", "raw": "I've created a dataset with example summaries of the 500 most liked datasets on the Hub: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/davanstrien/dataset-tldr", "href": null, "resource": { "type": "dataset", "id": "davanstrien/dataset-tldr", "discussionNum": null }, "url": "https://huggingface.co/datasets/davanstrien/dataset-tldr", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 these kinds of summaries be helpful? ", "raw": "Would these kinds of summaries be helpful? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

[]

[]

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[ { "type": "text", "value": "𝗠𝗮𝗷𝗼𝗿 𝗧𝗢𝗠: 𝗣𝗹𝗮𝗻𝗲𝘁 𝗘𝗮𝗿𝘁𝗵 𝗶𝘀 𝗯̶𝗹̶𝘂̶𝗲̶ 𝟱.𝟰𝟬𝟱 𝗚𝗛𝘇", "raw": "𝗠𝗮𝗷𝗼𝗿 𝗧𝗢𝗠: 𝗣𝗹𝗮𝗻𝗲𝘁 𝗘𝗮𝗿𝘁𝗵 𝗶𝘀 𝗯̶𝗹̶𝘂̶𝗲̶ 𝟱.𝟰𝟬𝟱 𝗚𝗛𝘇", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🚨 EXPANSION RELEASE: 𝗦𝗲𝗻𝘁𝗶𝗻𝗲𝗹-𝟭 𝗶𝘀 𝗻𝗼𝘄 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 in the MajorTOM-Core!", "raw": "🚨 EXPANSION RELEASE: 𝗦𝗲𝗻𝘁𝗶𝗻𝗲𝗹-𝟭 𝗶𝘀 𝗻𝗼𝘄 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 in the MajorTOM-Core!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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/Major-TOM/Core-S1RTC", "href": null, "resource": { "type": "dataset", "id": "Major-TOM/Core-S1RTC", "discussionNum": null }, "url": "https://huggingface.co/datasets/Major-TOM/Core-S1RTC", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🏎 Together with ", "raw": "🏎 Together with ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@aliFrancis", "href": null, "resource": null, "url": null, "code": null, "user": "aliFrancis", "label": null, "lang": null }, { "type": "text", "value": " we've been racing to release the first official expansion to the Major TOM project.", "raw": " we've been racing to release the first official expansion to the Major TOM project.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "MajorTOM-Core-S1RTC contains 1,469,955 of SAR images paired to Sentinel-2 images from Core-S2.", "raw": "MajorTOM-Core-S1RTC contains 1,469,955 of SAR images paired to Sentinel-2 images from Core-S2.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 cover more than 65% of the optical coverage with an average time shift of 7 days.", "raw": "🔍We cover more than 65% of the optical coverage with an average time shift of 7 days.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "16 TB of radiometrically calibrated SAR imagery, available in the exact same format as the existing Major-TOM data.", "raw": "16 TB of radiometrically calibrated SAR imagery, available in the exact same format as the existing Major-TOM 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": "🗺️ You can explore instantly in our viewing app:", "raw": "🗺️ You can explore instantly in our viewing 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": "resource", "value": null, "raw": "https://huggingface.co/spaces/Major-TOM/MajorTOM-Core-Viewer", "href": null, "resource": { "type": "space", "id": "Major-TOM/MajorTOM-Core-Viewer", "discussionNum": null }, "url": "https://huggingface.co/spaces/Major-TOM/MajorTOM-Core-Viewer", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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, what now?", "raw": "So, what now?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🧱 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 𝐆𝐫𝐨𝐰𝐭𝐡: our community continues to grow! To coordinate the upcoming expansions as well as use cases of the open data, we will organise a meet up on 23 April, you can 𝐫𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐲𝐨𝐮𝐫 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭 here: ", "raw": "🧱 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 𝐆𝐫𝐨𝐰𝐭𝐡: our community continues to grow! To coordinate the upcoming expansions as well as use cases of the open data, we will organise a meet up on 23 April, you can 𝐫𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐲𝐨𝐮𝐫 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭 here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://forms.gle/eBj8JvibJx9b6PLf9", "href": "https://forms.gle/eBj8JvibJx9b6PLf9", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 𝐃𝐚𝐭𝐚 𝐟𝐨𝐫 𝐎𝐩𝐞𝐧 𝐌𝐨𝐝𝐞𝐥𝐬: Major-TOM Core dataset is currently supporting several strands of ongoing research within and outwith our lab and we are looking forward to the time when we can release models that take advantage of that data! ", "raw": "🚂 𝐎𝐩𝐞𝐧 𝐃𝐚𝐭𝐚 𝐟𝐨𝐫 𝐎𝐩𝐞𝐧 𝐌𝐨𝐝𝐞𝐥𝐬: Major-TOM Core dataset is currently supporting several strands of ongoing research within and outwith our lab and we are looking forward to the time when we can release models that take advantage of that data! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/Major-TOM", "href": "https://huggingface.co/Major-TOM", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📌 𝐏𝐨𝐬𝐭𝐞𝐫 𝐚𝐭 𝐈𝐆𝐀𝐑𝐒𝐒: We will present Major TOM project as a poster at IGARSS in Athens (July) - come talk to us if you're there! You can access the paper here: ", "raw": "📌 𝐏𝐨𝐬𝐭𝐞𝐫 𝐚𝐭 𝐈𝐆𝐀𝐑𝐒𝐒: We will present Major TOM project as a poster at IGARSS in Athens (July) - come talk to us if you're there! You can access the paper here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2402.12095", "href": null, "resource": { "type": "paper", "id": "2402.12095", "discussionNum": null }, "url": "https://huggingface.co/papers/2402.12095", "code": null, "user": null, "label": "Major TOM: Expandable Datasets for Earth Observation (2402.12095)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🌌 Developed at European Space Agency Φ-lab in partnership with Hugging Face ", "raw": "🌌 Developed at European Space Agency Φ-lab in partnership with Hugging Face ", "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: Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms by ", "raw": "🔍 Today's pick in Interpretability & Analysis of LMs: Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@mwhanna", "href": null, "resource": null, "url": null, "code": null, "user": "mwhanna", "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": "@sandropezzelle", "href": null, "resource": null, "url": null, "code": null, "user": "sandropezzelle", "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": "@belinkov", "href": null, "resource": null, "url": null, "code": null, "user": "belinkov", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Edge attribution patching (EAP) is a circuit discovery technique using gradients to approximate the effects of causal intervening on each model edge. In the literature, its effectiveness is validated by comparing the overlap of its resulting circuits with those found via causal interventions (much more expensive).", "raw": "Edge attribution patching (EAP) is a circuit discovery technique using gradients to approximate the effects of causal intervening on each model edge. In the literature, its effectiveness is validated by comparing the overlap of its resulting circuits with those found via causal interventions (much more expensive).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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:", "raw": "This 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": "1. Proposes a new method for faithful and efficient circuit discovery named edge attribution patching with integrated gradients (EAP-IG)", "raw": "1. Proposes a new method for faithful and efficient circuit discovery named edge attribution patching with integrated gradients (EAP-IG)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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. Evaluates the faithfulness EAP, EAP-IG and activation patching, i.e. whether behavior of the model remains consistent after all non-circuit edges are ablated.", "raw": "2. Evaluates the faithfulness EAP, EAP-IG and activation patching, i.e. whether behavior of the model remains consistent after all non-circuit edges are ablated.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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. Highlights that, while the no-overlap and full-overlap of EAP-like methods with activation patching results are generally good indicators of unfaithful and faithful (respectively) circuit identification, circuits with moderate overlap cannot generally assumed to be faithful to model behavior.", "raw": "3. Highlights that, while the no-overlap and full-overlap of EAP-like methods with activation patching results are generally good indicators of unfaithful and faithful (respectively) circuit identification, circuits with moderate overlap cannot generally assumed to be faithful to model behavior.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 advantage of EAP-IG is enabling the usage of KL-Divergence as a target for gradient propagation, which is not possible in the case of raw gradient-based EAP. ", "raw": "An advantage of EAP-IG is enabling the usage of KL-Divergence as a target for gradient propagation, which is not possible in the case of raw gradient-based EAP. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "EAP-IG runtime is approximately similar to the one of EAP, with a small number of steps to approximate the gradient integral.", "raw": "EAP-IG runtime is approximately similar to the one of EAP, with a small number of steps to approximate the gradient integral.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Importantly, circuit faithfulness does not imply completeness, i.e. whether all components participating towards a specific task were accounted for. This aspect is identified as interesting for future work.", "raw": "Importantly, circuit faithfulness does not imply completeness, i.e. whether all components participating towards a specific task were accounted for. This aspect is identified as interesting for future 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": "📄 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/2403.17806", "href": null, "resource": { "type": "paper", "id": "2403.17806", "discussionNum": null }, "url": "https://huggingface.co/papers/2403.17806", "code": null, "user": null, "label": "Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding\n Model Mechanisms (2403.17806)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 daily picks: ", "raw": "🔍 All daily picks: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/gsarti/daily-picks-in-interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9", "href": null, "resource": { "type": "collection", "id": "gsarti/daily-picks-in-interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9", "discussionNum": null }, "url": "https://huggingface.co/collections/gsarti/daily-picks-in-interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Based on the work of ", "raw": "Based on the work of ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@mrinaldi", "href": null, "resource": null, "url": null, "code": null, "user": "mrinaldi", "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": "@ruggsea", "href": null, "resource": null, "url": null, "code": null, "user": "ruggsea", "label": null, "lang": null }, { "type": "text", "value": " we just released the biggest - ready for training - conversational dataset based on Usenet data in the Italian language 🇮🇹🇮🇹🇮🇹🇮🇹🇮🇹🇮🇹🇮🇹. It contains about 9 millions of conversations made by real humans. ", "raw": " we just released the biggest - ready for training - conversational dataset based on Usenet data in the Italian language 🇮🇹🇮🇹🇮🇹🇮🇹🇮🇹🇮🇹🇮🇹. It contains about 9 millions of conversations made by real humans. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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/mii-community/UsenetArchiveIT-conversations", "href": null, "resource": { "type": "dataset", "id": "mii-community/UsenetArchiveIT-conversations", "discussionNum": null }, "url": "https://huggingface.co/datasets/mii-community/UsenetArchiveIT-conversations", "code": null, "user": null, "label": 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": "Exciting news! 🎉 I've created the OpenCerebrum datasets, open-source alternatives to Aether Research's proprietary Cerebrum dataset.", "raw": "Exciting news! 🎉 I've created the OpenCerebrum datasets, open-source alternatives to Aether Research's proprietary Cerebrum 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": "The first, OpenCerebrum SFT, is a text-generation and question-answering dataset with ~1.2M examples, curated from sources like Open-Orca, glaiveai, camel-ai, and more! 📚", "raw": "The first, OpenCerebrum SFT, is a text-generation and question-answering dataset with ~1.2M examples, curated from sources like Open-Orca, glaiveai, camel-ai, and 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": "The second, OpenCerebrum DPO, is a smaller dataset with ~21k examples, focusing on data point optimization. It's curated from sources like jondurbin, argilla, grimulkan, and others. 📊", "raw": "The second, OpenCerebrum DPO, is a smaller dataset with ~21k examples, focusing on data point optimization. It's curated from sources like jondurbin, argilla, grimulkan, and others. 📊", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Both datasets are licensed under Apache-2.0 and are available in English. They're ready for use in your projects, and I welcome any feedback for future improvements! 🚀", "raw": "Both datasets are licensed under Apache-2.0 and are available in English. They're ready for use in your projects, and I welcome any feedback for future improvements! 🚀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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/Locutusque/OpenCerebrum-dpo", "href": null, "resource": { "type": "dataset", "id": "Locutusque/OpenCerebrum-dpo", "discussionNum": null }, "url": "https://huggingface.co/datasets/Locutusque/OpenCerebrum-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/datasets/Locutusque/OpenCerebrum-SFT", "href": null, "resource": { "type": "dataset", "id": "Locutusque/OpenCerebrum-SFT", "discussionNum": null }, "url": "https://huggingface.co/datasets/Locutusque/OpenCerebrum-SFT", "code": null, "user": null, "label": null, "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/Locutusque/OpenCerebrum-1.0-7b-SFT", "href": null, "resource": { "type": "model", "id": "Locutusque/OpenCerebrum-1.0-7b-SFT", "discussionNum": null }, "url": "https://huggingface.co/Locutusque/OpenCerebrum-1.0-7b-SFT", "code": null, "user": null, "label": null, "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/Locutusque/OpenCerebrum-1.0-7b-DPO", "href": null, "resource": { "type": "model", "id": "Locutusque/OpenCerebrum-1.0-7b-DPO", "discussionNum": null }, "url": "https://huggingface.co/Locutusque/OpenCerebrum-1.0-7b-DPO", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Current LLMs are very susceptible to generating toxic, harmful and even dangerous content. They can also generate outputs with gender or racial biases.", "raw": "Current LLMs are very susceptible to generating toxic, harmful and even dangerous content. They can also generate outputs with gender or racial biases.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Biden-Harris Executive Order (", "raw": "The Biden-Harris Executive Order (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence", "href": "https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") sets forth guidelines on what is considered a safe AI system.", "raw": ") sets forth guidelines on what is considered a safe AI 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": "Following up on these guidelines, we present the world's first open source Biden-Harris Executive Order Red teamed Multilingual Language Model: Aurora-M.", "raw": "Following up on these guidelines, we present the world's first open source Biden-Harris Executive Order Red teamed Multilingual Language Model: Aurora-M.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 model is trained on 5 languages: English, Hindi, Japanese, Vietnamese and Finnish.", "raw": "The model is trained on 5 languages: English, Hindi, Japanese, Vietnamese and Finnish.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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://huggingface.co/blog/mayank-mishra/aurora", "href": "https://huggingface.co/blog/mayank-mishra/aurora", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 coming out soon.", "raw": "Paper coming out soon.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Base model: ", "raw": "Base model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/aurora-m/aurora-m-base", "href": null, "resource": { "type": "model", "id": "aurora-m/aurora-m-base", "discussionNum": null }, "url": "https://huggingface.co/aurora-m/aurora-m-base", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " (not safety tuned)", "raw": " (not safety tuned)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Instruct model: ", "raw": "Instruct model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/aurora-m/aurora-m-instruct", "href": null, "resource": { "type": "model", "id": "aurora-m/aurora-m-instruct", "discussionNum": null }, "url": "https://huggingface.co/aurora-m/aurora-m-instruct", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " (not safety tuned)", "raw": " (not safety tuned)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Red teamed model: ", "raw": "Red teamed model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/aurora-m/aurora-m-biden-harris-redteamed", "href": null, "resource": { "type": "model", "id": "aurora-m/aurora-m-biden-harris-redteamed", "discussionNum": null }, "url": "https://huggingface.co/aurora-m/aurora-m-biden-harris-redteamed", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " (safety tuned according to the order mentioned above)", "raw": " (safety tuned according to the order mentioned above)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "A new paper introduces Visual CoT, a new approach that enhances multi-modal large language models with visual chain-of-thought reasoning capabilities. This allows language models to dynamically identify and focus on specific regions within images that are most relevant for answering questions, mimicking human-like efficient visual reasoning.", "raw": "A new paper introduces Visual CoT, a new approach that enhances multi-modal large language models with visual chain-of-thought reasoning capabilities. This allows language models to dynamically identify and focus on specific regions within images that are most relevant for answering questions, mimicking human-like efficient visual 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": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Keypoints:", "raw": "Keypoints:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Introduces the 373k Visual CoT dataset with bounding box annotations highlighting essential image regions", "raw": "* Introduces the 373k Visual CoT dataset with bounding box annotations highlighting essential image regions", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Proposes a multi-turn pipeline for focusing on relevant visual inputs", "raw": "* Proposes a multi-turn pipeline for focusing on relevant visual inputs", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 strong results on multi-modal benchmarks ", "raw": "* Achieves strong results on multi-modal 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/2403.16999", "href": null, "resource": { "type": "paper", "id": "2403.16999", "discussionNum": null }, "url": "https://huggingface.co/papers/2403.16999", "code": null, "user": null, "label": "Visual CoT: Unleashing Chain-of-Thought Reasoning in Multi-Modal\n Language Models (2403.16999)", "lang": null }, { "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, data and other resources: ", "raw": "Code, data and other resources: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/deepcs233/Visual-CoT", "href": "https://github.com/deepcs233/Visual-CoT", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 authors for their work!", "raw": "Congrats to the authors for their work!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗯𝗲𝗮𝗺 𝘀𝗲𝗮𝗿𝗰𝗵 𝗱𝗲𝗰𝗼𝗱𝗶𝗻𝗴 𝘄𝗼𝗿𝗸? ➡️ 𝙉𝙚𝙬 𝙫𝙞𝙨𝙪𝙖𝙡𝙞𝙯𝙖𝙩𝙞𝙤𝙣 𝙩𝙤𝙤𝙡! 👀", "raw": "𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗯𝗲𝗮𝗺 𝘀𝗲𝗮𝗿𝗰𝗵 𝗱𝗲𝗰𝗼𝗱𝗶𝗻𝗴 𝘄𝗼𝗿𝗸? ➡️ 𝙉𝙚𝙬 𝙫𝙞𝙨𝙪𝙖𝙡𝙞𝙯𝙖𝙩𝙞𝙤𝙣 𝙩𝙤𝙤𝙡! 👀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 Decoder-type LLMs like GPT4 or Mistral-Large, the output is generated one token (=word part) at a time. That's why they're nicknamed \"stochastic parrots\": the \"thinking\" process only happens one step at a time, so it can seem really myopic.", "raw": "In Decoder-type LLMs like GPT4 or Mistral-Large, the output is generated one token (=word part) at a time. That's why they're nicknamed \"stochastic parrots\": the \"thinking\" process only happens one step at a time, so it can seem really myopic.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 𝐡𝐨𝐰 𝐢𝐬 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝐭𝐨𝐤𝐞𝐧 𝐬𝐞𝐥𝐞𝐜𝐭𝐞𝐝?", "raw": "𝐒𝐨 𝐡𝐨𝐰 𝐢𝐬 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝐭𝐨𝐤𝐞𝐧 𝐬𝐞𝐥𝐞𝐜𝐭𝐞𝐝?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📊 Given its input sentence like \"𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 7𝘵𝘩 𝘍𝘪𝘣𝘰𝘯𝘢𝘤𝘤𝘪 𝘯𝘶𝘮𝘣𝘦𝘳? 𝘛𝘩𝘦 7𝘵𝘩 𝘍𝘪𝘣𝘰𝘯𝘢𝘤𝘤𝘪 𝘯𝘶𝘮𝘣𝘦𝘳\", the Decoder LLM generates, for each token in its vocabulary, a score that represents this token's probability of coming next.", "raw": "📊 Given its input sentence like \"𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 7𝘵𝘩 𝘍𝘪𝘣𝘰𝘯𝘢𝘤𝘤𝘪 𝘯𝘶𝘮𝘣𝘦𝘳? 𝘛𝘩𝘦 7𝘵𝘩 𝘍𝘪𝘣𝘰𝘯𝘢𝘤𝘤𝘪 𝘯𝘶𝘮𝘣𝘦𝘳\", the Decoder LLM generates, for each token in its vocabulary, a score that represents this token's probability of coming next.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 instance: \"𝙞𝙨\" gets score 0.56, and \"𝙘𝙖𝙣\" gets score 0.35.", "raw": "For instance: \"𝙞𝙨\" gets score 0.56, and \"𝙘𝙖𝙣\" gets score 0.35.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🤑 𝐆𝐫𝐞𝐞𝐝𝐲 𝐝𝐞𝐜𝐨𝐝𝐢𝐧𝐠 is the naive option where you simply take the next most probable token at each step. But this creates paths that maximize very short-term rewards, thus may overlook better paths for the long term (like this time when you played FIFA all evening and arrived unprepared to your school exam on the next day).", "raw": "🤑 𝐆𝐫𝐞𝐞𝐝𝐲 𝐝𝐞𝐜𝐨𝐝𝐢𝐧𝐠 is the naive option where you simply take the next most probable token at each step. But this creates paths that maximize very short-term rewards, thus may overlook better paths for the long term (like this time when you played FIFA all evening and arrived unprepared to your school exam on the next 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": "In our example, the next highest score token might be \"𝙞𝙨\", but this will strongly bias the LLM towards giving an hasty response. On the opposite, starting with \"𝙘𝙖𝙣\" could have been completed with \"𝘣𝘦 𝘰𝘣𝘵𝘢𝘪𝘯𝘦𝘥 𝘧𝘳𝘰𝘮 𝘤𝘰𝘮𝘱𝘶𝘵𝘪𝘯𝘨 𝘱𝘳𝘦𝘷𝘪𝘰𝘶𝘴 𝘍𝘪𝘣𝘰𝘯𝘢𝘤𝘤𝘪 𝘯𝘶𝘮𝘣𝘦𝘳𝘴 𝘧𝘪𝘳𝘴𝘵\", which steers the LLM towards a correct reasoning!", "raw": "In our example, the next highest score token might be \"𝙞𝙨\", but this will strongly bias the LLM towards giving an hasty response. On the opposite, starting with \"𝙘𝙖𝙣\" could have been completed with \"𝘣𝘦 𝘰𝘣𝘵𝘢𝘪𝘯𝘦𝘥 𝘧𝘳𝘰𝘮 𝘤𝘰𝘮𝘱𝘶𝘵𝘪𝘯𝘨 𝘱𝘳𝘦𝘷𝘪𝘰𝘶𝘴 𝘍𝘪𝘣𝘰𝘯𝘢𝘤𝘤𝘪 𝘯𝘶𝘮𝘣𝘦𝘳𝘴 𝘧𝘪𝘳𝘴𝘵\", which steers the LLM towards a correct 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": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🗺️ 𝐁𝐞𝐚𝐦 𝐬𝐞𝐚𝐫𝐜𝐡 improves on greedy decoding by generating at each step several paths - called beams - instead of one. This allows the generation to explore a much larger space, thus find better completions. In our example, both the \"𝙞𝙨\" and the \"𝙘𝙖𝙣\" completion could be tested. ✅", "raw": "🗺️ 𝐁𝐞𝐚𝐦 𝐬𝐞𝐚𝐫𝐜𝐡 improves on greedy decoding by generating at each step several paths - called beams - instead of one. This allows the generation to explore a much larger space, thus find better completions. In our example, both the \"𝙞𝙨\" and the \"𝙘𝙖𝙣\" completion could be tested. ✅", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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've created a tool to let you visualize it, thank you ", "raw": "👉 I've created a tool to let you visualize it, thank you ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@joaogante", "href": null, "resource": null, "url": null, "code": null, "user": "joaogante", "label": null, "lang": null }, { "type": "text", "value": " for your great help!", "raw": " for your great 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": "𝙏𝙧𝙮 𝙞𝙩 𝙝𝙚𝙧𝙚: ", "raw": "𝙏𝙧𝙮 𝙞𝙩 𝙝𝙚𝙧𝙚: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/m-ric/beam_search_visualizer", "href": null, "resource": { "type": "space", "id": "m-ric/beam_search_visualizer", "discussionNum": null }, "url": "https://huggingface.co/spaces/m-ric/beam_search_visualizer", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Very glad to welcome ", "raw": "Very glad to welcome ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@josefprusa", "href": null, "resource": null, "url": null, "code": null, "user": "josefprusa", "label": null, "lang": null }, { "type": "text", "value": ", pioneer of 3D printing and open source hardware, founder of ", "raw": ", pioneer of 3D printing and open source hardware, founder of ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.prusa3d.com/", "href": "https://www.prusa3d.com/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ", to the HF Hub 👋", "raw": ", to the HF Hub 👋", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "AI applied to 3D printing could be big.", "raw": "AI applied to 3D printing could be big.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "LLM Agent Operating System", "raw": "LLM Agent Operating 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": "resource", "value": null, "raw": "https://huggingface.co/papers/2403.16971", "href": null, "resource": { "type": "paper", "id": "2403.16971", "discussionNum": null }, "url": "https://huggingface.co/papers/2403.16971", "code": null, "user": null, "label": "LLM Agent Operating System (2403.16971)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 integration and deployment of large language model (LLM)-based intelligent agents have been fraught with challenges that compromise their efficiency and efficacy. Among these issues are sub-optimal scheduling and resource allocation of agent requests over the LLM, the difficulties in maintaining context during interactions between agent and LLM, and the complexities inherent in integrating heterogeneous agents with different capabilities and specializations. The rapid increase of agent quantity and complexity further exacerbates these issues, often leading to bottlenecks and sub-optimal utilization of resources. Inspired by these challenges, this paper presents AIOS, an LLM agent operating system, which embeds large language model into operating systems (OS). Specifically, AIOS is designed to optimize resource allocation, facilitate context switch across agents, enable concurrent execution of agents, provide tool service for agents, and maintain access control for agents. We present the architecture of such an operating system, outline the core challenges it aims to resolve, and provide the basic design and implementation of the AIOS. Our experiments on concurrent execution of multiple agents demonstrate the reliability and efficiency of our AIOS modules. Through this, we aim to not only improve the performance and efficiency of LLM agents but also to pioneer for better development and deployment of the AIOS ecosystem in the future.", "raw": "The integration and deployment of large language model (LLM)-based intelligent agents have been fraught with challenges that compromise their efficiency and efficacy. Among these issues are sub-optimal scheduling and resource allocation of agent requests over the LLM, the difficulties in maintaining context during interactions between agent and LLM, and the complexities inherent in integrating heterogeneous agents with different capabilities and specializations. The rapid increase of agent quantity and complexity further exacerbates these issues, often leading to bottlenecks and sub-optimal utilization of resources. Inspired by these challenges, this paper presents AIOS, an LLM agent operating system, which embeds large language model into operating systems (OS). Specifically, AIOS is designed to optimize resource allocation, facilitate context switch across agents, enable concurrent execution of agents, provide tool service for agents, and maintain access control for agents. We present the architecture of such an operating system, outline the core challenges it aims to resolve, and provide the basic design and implementation of the AIOS. Our experiments on concurrent execution of multiple agents demonstrate the reliability and efficiency of our AIOS modules. Through this, we aim to not only improve the performance and efficiency of LLM agents but also to pioneer for better development and deployment of the AIOS ecosystem in the future.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "MLX RAG with GGUF Models", "raw": "MLX RAG with GGUF 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": "text", "value": "Minimal, clean code implementation of RAG with mlx inferencing for GGUF models.", "raw": "Minimal, clean code implementation of RAG with mlx inferencing for GGUF 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": "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/Jaykef/mlx-rag-gguf", "href": "https://github.com/Jaykef/mlx-rag-gguf", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 code here builds on vegaluisjose's example, it has been optimized to support RAG-based inferencing for .gguf models. I am using BAAI/bge-small-en for the embedding model, tinyllama-1.1b-chat-v1.0.Q4_0.gguf as base model and the custom vector database script for indexing texts in a pdf file. Inference speeds can go up to ~413 tokens/sec for prompts and ~36 tokens/sec for generation on my M2 Air.", "raw": "The code here builds on vegaluisjose's example, it has been optimized to support RAG-based inferencing for .gguf models. I am using BAAI/bge-small-en for the embedding model, tinyllama-1.1b-chat-v1.0.Q4_0.gguf as base model and the custom vector database script for indexing texts in a pdf file. Inference speeds can go up to ~413 tokens/sec for prompts and ~36 tokens/sec for generation on my M2 Air.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Queries make use of both .gguf (base model) and .npz (retrieval model) simultaneouly resulting in much higher inferencing speeds.", "raw": "Queries make use of both .gguf (base model) and .npz (retrieval model) simultaneouly resulting in much higher inferencing speeds.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Now You Can Full Fine Tune / DreamBooth Stable Diffusion XL (SDXL) with only 10.3 GB VRAM via OneTrainer — Both U-NET and Text Encoder 1 is trained — Compared 14 GB config vs slower 10.3 GB Config", "raw": "Now You Can Full Fine Tune / DreamBooth Stable Diffusion XL (SDXL) with only 10.3 GB VRAM via OneTrainer — Both U-NET and Text Encoder 1 is trained — Compared 14 GB config vs slower 10.3 GB Config", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Full config and instructions are shared here : ", "raw": "Full config and instructions are shared here : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/96028218", "href": "https://www.patreon.com/posts/96028218", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Used SG161222/RealVisXL_V4.0 as a base model and OneTrainer to train on Windows 10 : ", "raw": "Used SG161222/RealVisXL_V4.0 as a base model and OneTrainer to train on Windows 10 : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/Nerogar/OneTrainer", "href": "https://github.com/Nerogar/OneTrainer", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 posted example x/y/z checkpoint comparison images are not cherry picked. So I can get perfect images with multiple tries.", "raw": "The posted example x/y/z checkpoint comparison images are not cherry picked. So I can get perfect images with multiple tries.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 150 epochs, 15 images and used my ground truth 5200 regularization images : ", "raw": "Trained 150 epochs, 15 images and used my ground truth 5200 regularization images : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/massive-4k-woman-87700469", "href": "https://www.patreon.com/posts/massive-4k-woman-87700469", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 each epoch only 15 of regularization images used to make DreamBooth training affect", "raw": "In each epoch only 15 of regularization images used to make DreamBooth training affect", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 a caption only “ohwx man” is used, for regularization images just “man”", "raw": "As a caption only “ohwx man” is used, for regularization images just “man”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 download configs and full instructions here : ", "raw": "You can download configs and full instructions here : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/96028218", "href": "https://www.patreon.com/posts/96028218", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Hopefully full public tutorial coming within 2 weeks. I will show all configuration as well", "raw": "Hopefully full public tutorial coming within 2 weeks. I will show all configuration 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 }, { "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 tutorial will be on our channel : ", "raw": "The tutorial will be on our channel : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.youtube.com/SECourses", "href": "https://www.youtube.com/SECourses", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Training speeds are as below thus durations:", "raw": "Training speeds are as below thus durations:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "RTX 3060 — slow preset : 3.72 second / it thus 15 train images 150 epoch 2 (reg images concept) : 4500 steps = 4500 3.72 / 3600 = 4.6 hours", "raw": "RTX 3060 — slow preset : 3.72 second / it thus 15 train images 150 epoch 2 (reg images concept) : 4500 steps = 4500 3.72 / 3600 = 4.6 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": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "RTX 3090 TI — slow preset : 1.58 second / it thus : 4500 * 1.58 / 3600 = 2 hours", "raw": "RTX 3090 TI — slow preset : 1.58 second / it thus : 4500 * 1.58 / 3600 = 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": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "RTX 3090 TI — fast preset : 1.45 second / it thus : 4500 * 1.45 / 3600 = 1.8 hours", "raw": "RTX 3090 TI — fast preset : 1.45 second / it thus : 4500 * 1.45 / 3600 = 1.8 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": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "A quick tutorial for how to use concepts in OneTrainer : ", "raw": "A quick tutorial for how to use concepts in OneTrainer : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/yPOadldf6bI", "href": "https://youtu.be/yPOadldf6bI", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": 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": "The AI Comic Factory 1.2 has been released a few hours ago!", "raw": "The AI Comic Factory 1.2 has been released a few hours 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": "You can now tell the LLM in advance how many pages you will want, for richer and deeper stories ", "raw": "You can now tell the LLM in advance how many pages you will want, for richer and deeper 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": "link", "value": null, "raw": "https://www.loom.com/share/b8b2d55fc60249a78df60adaa2673d2f", "href": "https://www.loom.com/share/b8b2d55fc60249a78df60adaa2673d2f", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 the future I would like to improve how layouts are attributed to pages ( seeing the same layout 6x can be a bit boring)", "raw": "I the future I would like to improve how layouts are attributed to pages ( seeing the same layout 6x can be a bit boring)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Also, I'm still waiting for some new improved AI models to drop, so stay tuned!", "raw": "Also, I'm still waiting for some new improved AI models to drop, so stay tuned!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": 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": "Introducing Marigold-LCM 🌼 — a FAST version of the now popular state-of-the-art depth estimator! Thanks to the latent consistency distillation, it retains the precision of the original Marigold but reaches the solution in just a few steps! ", "raw": "Introducing Marigold-LCM 🌼 — a FAST version of the now popular state-of-the-art depth estimator! Thanks to the latent consistency distillation, it retains the precision of the original Marigold but reaches the solution in just a few 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": "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 teaser video attached below and play with the new demo - it accepts videos now! Also, meet the new team member: Tianfu Wang (", "raw": "Check out the teaser video attached below and play with the new demo - it accepts videos now! Also, meet the new team member: Tianfu Wang (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Tianfwang", "href": null, "resource": null, "url": null, "code": null, "user": "Tianfwang", "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": "🤗 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/prs-eth/marigold-lcm", "href": null, "resource": { "type": "space", "id": "prs-eth/marigold-lcm", "discussionNum": null }, "url": "https://huggingface.co/spaces/prs-eth/marigold-lcm", "code": null, "user": null, "label": null, "lang": null }, { "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": "link", "value": null, "raw": "https://huggingface.co/prs-eth/marigold-lcm-v1-0", "href": "https://huggingface.co/prs-eth/marigold-lcm-v1-0", "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": "text", "value": "🤗 Original Marigold post: ", "raw": "🤗 Original Marigold post: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/posts/toshas/656973498012745", "href": "https://huggingface.co/posts/toshas/656973498012745", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/2312.02145", "href": null, "resource": { "type": "paper", "id": "2312.02145", "discussionNum": null }, "url": "https://huggingface.co/papers/2312.02145", "code": null, "user": null, "label": "Repurposing Diffusion-Based Image Generators for Monocular Depth\n Estimation (2312.02145)", "lang": null }, { "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://marigoldmonodepth.github.io", "href": "https://marigoldmonodepth.github.io", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/prs-eth/marigold", "href": "https://github.com/prs-eth/marigold", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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: pip install diffusers", "raw": "👾 Code: pip install diffusers", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Does anyone have experience with finetuning Gemma? Even the 2B variant feels more memory heavy than mistral 7B. I know that its vocabulary is much larger (250k) but I'm a bit surprised that the max batch size that I can get in an A100 80GB is only 2 whereas I could fit 4 with mistral 7B - even though Gemma is much smaller except for the embedding layer. Both runs were using FA, same sequence length, same deepspeed zero 3 settings. Oh and yes I'm using the most recent hot fix of transformers that solves a memory issue with Gemma and others. ", "raw": "Does anyone have experience with finetuning Gemma? Even the 2B variant feels more memory heavy than mistral 7B. I know that its vocabulary is much larger (250k) but I'm a bit surprised that the max batch size that I can get in an A100 80GB is only 2 whereas I could fit 4 with mistral 7B - even though Gemma is much smaller except for the embedding layer. Both runs were using FA, same sequence length, same deepspeed zero 3 settings. Oh and yes I'm using the most recent hot fix of transformers that solves a memory issue with Gemma and others. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 prior experience that you can share or suggestions to improve throughout? ", "raw": "Any prior experience that you can share or suggestions to improve throughout? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Today we launch our space in colab with ", "raw": "Today we launch our space in colab with ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@dvilasuero", "href": null, "resource": null, "url": null, "code": null, "user": "dvilasuero", "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": "@davanstrien", "href": null, "resource": null, "url": null, "code": null, "user": "davanstrien", "label": null, "lang": null }, { "type": "text", "value": " so you can help us translate/correct our curated prompt dataset, that will be used to evaluate the performance of Arabic LLMs later and help our community to identify how open models perform on Arabic.", "raw": " so you can help us translate/correct our curated prompt dataset, that will be used to evaluate the performance of Arabic LLMs later and help our community to identify how open models perform on Arabic.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 to Get Involved?", "raw": "How to Get Involved?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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. Visit our Argilla Space and start reviewing prompts.", "raw": "1. Visit our Argilla Space and start reviewing prompts.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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://2a2i-prompt-translation-for-arabic.hf.space/", "href": "https://2a2i-prompt-translation-for-arabic.hf.space/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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. Join our Discord channel in the HuggingFace's discord server to connect with the community and share your insights.", "raw": "2. Join our Discord channel in the HuggingFace's discord server to connect with the community and share your insights.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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://discord.com/channels/879548962464493619/1217179730869096448", "href": "https://discord.com/channels/879548962464493619/1217179730869096448", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "MathVerse", "raw": "MathVerse", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?", "raw": "Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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/2403.14624", "href": null, "resource": { "type": "paper", "id": "2403.14624", "discussionNum": null }, "url": "https://huggingface.co/papers/2403.14624", "code": null, "user": null, "label": "MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual\n Math Problems? (2403.14624)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 remarkable progress of Multi-modal Large Language Models (MLLMs) has garnered unparalleled attention, due to their superior performance in visual contexts. However, their capabilities in visual math problem-solving remain insufficiently evaluated and understood. We investigate current benchmarks to incorporate excessive visual content within textual questions, which potentially assist MLLMs in deducing answers without truly interpreting the input diagrams. To this end, we introduce MathVerse, an all-around visual math benchmark designed for an equitable and in-depth evaluation of MLLMs. We meticulously collect 2,612 high-quality, multi-subject math problems with diagrams from publicly available sources. Each problem is then transformed by human annotators into six distinct versions, each offering varying degrees of information content in multi-modality, contributing to 15K test samples in total. This approach allows MathVerse to comprehensively assess whether and how much MLLMs can truly understand the visual diagrams for mathematical reasoning. In addition, we propose a Chain-of-Thought (CoT) evaluation strategy for a fine-grained assessment of the output answers. Rather than naively judging True or False, we employ GPT-4(V) to adaptively extract crucial reasoning steps, and then score each step with detailed error analysis, which can reveal the intermediate CoT reasoning quality by MLLMs. We hope the MathVerse benchmark may provide unique insights to guide the future development of MLLMs.", "raw": "The remarkable progress of Multi-modal Large Language Models (MLLMs) has garnered unparalleled attention, due to their superior performance in visual contexts. However, their capabilities in visual math problem-solving remain insufficiently evaluated and understood. We investigate current benchmarks to incorporate excessive visual content within textual questions, which potentially assist MLLMs in deducing answers without truly interpreting the input diagrams. To this end, we introduce MathVerse, an all-around visual math benchmark designed for an equitable and in-depth evaluation of MLLMs. We meticulously collect 2,612 high-quality, multi-subject math problems with diagrams from publicly available sources. Each problem is then transformed by human annotators into six distinct versions, each offering varying degrees of information content in multi-modality, contributing to 15K test samples in total. This approach allows MathVerse to comprehensively assess whether and how much MLLMs can truly understand the visual diagrams for mathematical reasoning. In addition, we propose a Chain-of-Thought (CoT) evaluation strategy for a fine-grained assessment of the output answers. Rather than naively judging True or False, we employ GPT-4(V) to adaptively extract crucial reasoning steps, and then score each step with detailed error analysis, which can reveal the intermediate CoT reasoning quality by MLLMs. We hope the MathVerse benchmark may provide unique insights to guide the future development of 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 } ]

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[ { "type": "text", "value": "🚀🎭🌟 New Research Alert - CVPR 2024! 🌟 🎭🚀", "raw": "🚀🎭🌟 New Research Alert - CVPR 2024! 🌟 🎭🚀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Title: GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians 🔝", "raw": "📄 Title: GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians 🔝", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📝 Description: GaussianAvatars proposes a novel method for creating photorealistic and fully controllable head avatars by combining a parametric morphable face model with a dynamic 3D representation based on rigged 3D Gaussian splats, enabling high-quality rendering and precise animation control.", "raw": "📝 Description: GaussianAvatars proposes a novel method for creating photorealistic and fully controllable head avatars by combining a parametric morphable face model with a dynamic 3D representation based on rigged 3D Gaussian splats, enabling high-quality rendering and precise animation control.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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: Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Giebenhain, Matthias Nießner", "raw": "👥 Authors: Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Giebenhain, Matthias Nießner", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📅 Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA 🇺🇸", "raw": "📅 Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA 🇺🇸", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", 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null, "raw": "@DmitryRyumin", "href": null, "resource": null, "url": null, "code": null, "user": "DmitryRyumin", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🚀 Added to the Avatars Collection: ", "raw": "🚀 Added to the Avatars Collection: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "href": null, "resource": { "type": "collection", "id": "DmitryRyumin/avatars-65df37cdf81fec13d4dbac36", "discussionNum": null }, "url": 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[ { "type": "text", "value": "🏅 Quantized Embeddings are here! Unlike model quantization, embedding quantization is a post-processing step for embeddings that converts e.g. ", "raw": "🏅 Quantized Embeddings are here! Unlike model quantization, embedding quantization is a post-processing step for embeddings that converts e.g. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`float32`", "href": null, "resource": null, "url": null, "code": "float32", "user": null, "label": null, "lang": null }, { "type": "text", "value": " embeddings to binary or ", "raw": " embeddings to binary or ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`int8`", "href": null, "resource": null, "url": null, "code": "int8", "user": null, "label": null, "lang": null }, { "type": "text", "value": " embeddings. This saves 32x or 4x memory & disk space, and these embeddings are much easier to compare!", "raw": " embeddings. This saves 32x or 4x memory & disk space, and these embeddings are much easier to compare!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 results show 25-45x speedups in retrieval compared to full-size embeddings, while keeping 96% of the performance!", "raw": "Our results show 25-45x speedups in retrieval compared to full-size embeddings, while keeping 96% of the performance!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Learn more about it in our blogpost in collaboration with mixedbread.ai: ", "raw": "Learn more about it in our blogpost in collaboration with mixedbread.ai: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/embedding-quantization", "href": "https://huggingface.co/blog/embedding-quantization", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 try out our demo where we use quantized embeddings to let you search all of Wikipedia (yes, 41,000,000 texts) in 1 second on a CPU Space: ", "raw": "Or try out our demo where we use quantized embeddings to let you search all of Wikipedia (yes, 41,000,000 texts) in 1 second on a CPU Space: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/sentence-transformers/quantized-retrieval", "href": null, "resource": { "type": "space", "id": "sentence-transformers/quantized-retrieval", "discussionNum": null }, "url": "https://huggingface.co/spaces/sentence-transformers/quantized-retrieval", "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🔥🆕🆕🔥 Dataset Drop: 4 KTO signal transformed versions of the highly loved Argilla DPO datasets.", "raw": "🔥🆕🆕🔥 Dataset Drop: 4 KTO signal transformed versions of the highly loved Argilla DPO datasets.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "KTO formats for:", "raw": "KTO formats for:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- UltraFeedback Cleaned Binarized", "raw": "- UltraFeedback Cleaned Binarized", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Distilabel Intel Orca", "raw": "- Distilabel Intel Orca", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Distilabel Capybara", "raw": "- Distilabel Capybara", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- DPO mix", "raw": "- DPO mix", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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/collections/argilla/preference-datasets-for-kto-65f98314d7c1b04ab54d41a7", "href": null, "resource": { "type": "collection", "id": "argilla/preference-datasets-for-kto-65f98314d7c1b04ab54d41a7", "discussionNum": null }, "url": "https://huggingface.co/collections/argilla/preference-datasets-for-kto-65f98314d7c1b04ab54d41a7", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 claims :) ", "raw": "Paper claims :) ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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/2402.01306", "href": "https://arxiv.org/abs/2402.01306", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "KTO matches or exceeds DPO performance at scales from 1B to 30B parameters.1 That is, taking a preference dataset of n DPO pairs and breaking it up into 2n examples for KTO can yield better generations, despite the model ostensibly learning from a weaker signal.", "raw": "KTO matches or exceeds DPO performance at scales from 1B to 30B parameters.1 That is, taking a preference dataset of n DPO pairs and breaking it up into 2n examples for KTO can yield better generations, despite the model ostensibly learning from a weaker signal.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "KTO can handle extreme data imbalances, matching DPO performance while using up to 90% fewer desirable examples (i.e., examples of good generations). Its success thus cannot be ascribed to the alignment data being sourced from a preference dataset.", "raw": "KTO can handle extreme data imbalances, matching DPO performance while using up to 90% fewer desirable examples (i.e., examples of good generations). Its success thus cannot be ascribed to the alignment data being sourced from a preference 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": "When the pretrained model is sufficiently good, one can skip supervised finetuning and go straight to KTO without a loss in generation quality. In contrast, we find that without doing SFT first, DPO-aligned models are significantly worse at all scales.", "raw": "When the pretrained model is sufficiently good, one can skip supervised finetuning and go straight to KTO without a loss in generation quality. In contrast, we find that without doing SFT first, DPO-aligned models are significantly worse at all scales.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 you need something custom? Take a look at ", "raw": "Do you need something custom? Take a look at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@davanstrien", "href": null, "resource": null, "url": null, "code": null, "user": "davanstrien", "label": null, "lang": null }, { "type": "text", "value": " his guide on creating your own KTO dataset with Argilla and our community.", "raw": " his guide on creating your own KTO dataset with Argilla and our 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": "link", "value": null, "raw": "https://github.com/huggingface/data-is-better-together/tree/main/kto-preference", "href": "https://github.com/huggingface/data-is-better-together/tree/main/kto-preference", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🤗 PEFT v0.10.0 release! 🔥🚀✨", "raw": "🤗 PEFT v0.10.0 release! 🔥🚀✨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Some highli📝ghts:", "raw": "Some highli📝ghts:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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. FSDP+QLoRA and DeepSpeed Stage-3+QLoRA", "raw": "1. FSDP+QLoRA and DeepSpeed Stage-3+QLoRA", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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. Layer expansion + LoRA", "raw": "2. Layer expansion + LoRA", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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. DoRA support for Conv2D layers and quantized bitsandbytes layers", "raw": "3. DoRA support for Conv2D layers and quantized bitsandbytes layers", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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. New LoftQ utility", "raw": "4. New LoftQ utility", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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. Batched inference for mixed LoRA adapters.", "raw": "5. Batched inference for mixed LoRA adapters.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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": "http://Answer.AI", "href": "http://Answer.AI", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " team in collaboration with bitsandbytes and Hugging Face 🤗 open sourced code enabling the usage of FSDP+QLoRA and explained the whole process in their insightful blogpost ", "raw": " team in collaboration with bitsandbytes and Hugging Face 🤗 open sourced code enabling the usage of FSDP+QLoRA and explained the whole process in their insightful blogpost ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://lnkd.in/g6jgfXyv", "href": "https://lnkd.in/g6jgfXyv", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ". This is now integrated into Hugging Face ecosystem.", "raw": ". This is now integrated into Hugging Face ecosystem.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 an end-to-end example on FSDP+QLoRA, please refer ", "raw": "For an end-to-end example on FSDP+QLoRA, please refer ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://lnkd.in/gT3yY-Rx", "href": "https://lnkd.in/gT3yY-Rx", "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": "For an end-to-end example on DeepSpeed Stage-3+QLoRA, please refer ", "raw": "For an end-to-end example on DeepSpeed Stage-3+QLoRA, please refer ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://lnkd.in/gkt-xZRE", "href": "https://lnkd.in/gkt-xZRE", "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": "With the PR ", "raw": "With the PR ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://lnkd.in/g5F348MN", "href": "https://lnkd.in/g5F348MN", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " these changes are now upstreamed in ", "raw": " these changes are now upstreamed in ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://lnkd.in/g5_MxYtY", "href": "https://lnkd.in/g5_MxYtY", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " thanks to Wing Lian ! 🚀", "raw": " thanks to Wing Lian ! 🚀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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": "link", "value": null, "raw": "http://Answer.AI", "href": "http://Answer.AI", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " team, Titus von Köller , Younes Belkada, Benjamin Bossan and Zachary Mueller for all the help without which this couldn't have been possible. 🤗", "raw": " team, Titus von Köller , Younes Belkada, Benjamin Bossan and Zachary Mueller for all the help without which this couldn't have been 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": "For efficient depthwise layer expansion akin to ", "raw": "For efficient depthwise layer expansion akin to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`passthrough`", "href": null, "resource": null, "url": null, "code": "passthrough", "user": null, "label": null, "lang": null }, { "type": "text", "value": " method of ", "raw": " method of ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`mergekit`", "href": null, "resource": null, "url": null, "code": "mergekit", "user": null, "label": null, "lang": null }, { "type": "text", "value": " but without using additional memory and attaching LoRAs to it, refer to the details below! 🔥https://lnkd.in/ge95ztjA", "raw": " but without using additional memory and attaching LoRAs to it, refer to the details below! 🔥https://lnkd.in/ge95ztjA", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 DoRA is supported for Conv2D layers as well as bitsandbytes quantized layers ✨. For more details, please refer the below thread.", "raw": "Now DoRA is supported for Conv2D layers as well as bitsandbytes quantized layers ✨. For more details, please refer the below thread.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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://lnkd.in/gsJbuWPD", "href": "https://lnkd.in/gsJbuWPD", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 you can mix different LoRA adapters in a batch during inference which speeds-up the inference by avoiding computation of base model multiple times which would be the case for adaptive inference with batch_size=1! ⚡️.", "raw": "Now you can mix different LoRA adapters in a batch during inference which speeds-up the inference by avoiding computation of base model multiple times which would be the case for adaptive inference with batch_size=1! ⚡️.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 below. ", "raw": "Details below. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://lnkd.in/gD-pcX_B", "href": "https://lnkd.in/gD-pcX_B", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "LoftQ reduces quantization error by appropriately initializing the LoRA adapter weights. Normally, this is a two-step process. Benjamin Bossan", "raw": "LoftQ reduces quantization error by appropriately initializing the LoRA adapter weights. Normally, this is a two-step process. Benjamin Bossan", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "added new util ", "raw": "added new util ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`replace_lora_weights_loftq`", "href": null, "resource": null, "url": null, "code": "replace_lora_weights_loftq", "user": null, "label": null, "lang": null }, { "type": "text", "value": " for LoftQ to use it on the fly with bnb.", "raw": " for LoftQ to use it on the fly with bnb.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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, refer to the release notes. 📝", "raw": "For more details, refer to the release notes. 📝", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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://lnkd.in/gg7-AmHA", "href": "https://lnkd.in/gg7-AmHA", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ". As always, make sure losses go down and be happy to watch your model train!", "raw": ". As always, make sure losses go down and be happy to watch your model train!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Look at the beauty in the video — four different embeddings on the same map! In another community blog post, I explore how you can use Nomic Atlas to view and clean your dataset. You can check it out here - ", "raw": "Look at the beauty in the video — four different embeddings on the same map! In another community blog post, I explore how you can use Nomic Atlas to view and clean your dataset. You can check it out here - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/visheratin/nomic-data-cleaning", "href": "https://huggingface.co/blog/visheratin/nomic-data-cleaning", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "How to let LLM acquire the Agentic Capability?", "raw": "How to let LLM acquire the Agentic Capability?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Previous answers are direct imitation learning by collecting agentic data such as tool calling history (inefficient and introduces format hallucination). ", "raw": "Previous answers are direct imitation learning by collecting agentic data such as tool calling history (inefficient and introduces format hallucination). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Agent-FLAN tells a different view:", "raw": "Agent-FLAN tells a different view:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Eliciting the foundational capability (e.g., reasoning, retrieval, and instruction following) is more important", "raw": "- Eliciting the foundational capability (e.g., reasoning, retrieval, and instruction following) is more important", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Using chat data is more effective with less side effects than tool calling history", "raw": "- Using chat data is more effective with less side effects than tool calling history", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/internlm/Agent-FLAN", "href": null, "resource": { "type": "dataset", "id": "internlm/Agent-FLAN", "discussionNum": null }, "url": "https://huggingface.co/datasets/internlm/Agent-FLAN", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "HF Model: ", "raw": "HF Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/internlm/Agent-FLAN-7b", "href": null, "resource": { "type": "model", "id": "internlm/Agent-FLAN-7b", "discussionNum": null }, "url": "https://huggingface.co/internlm/Agent-FLAN-7b", "code": null, "user": null, "label": null, "lang": null }, { "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/2403.12881", "href": null, "resource": { "type": "paper", "id": "2403.12881", "discussionNum": null }, "url": "https://huggingface.co/papers/2403.12881", "code": null, "user": null, "label": "Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for\n Large Language Models (2403.12881)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Project page：https://internlm.github.io/Agent-FLAN/", "raw": "Project page：https://internlm.github.io/Agent-FLAN/", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "Here it is, an Adaptive AI Assistant (AAA) only made possible through open-source, incredibly inspiring, well done to the Open Interpreter team👏🏼👏🏼", "raw": "Here it is, an Adaptive AI Assistant (AAA) only made possible through open-source, incredibly inspiring, well done to the Open Interpreter team👏🏼👏🏼", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Glad this one did not start with big tech :)", "raw": "Glad this one did not start with big tech :)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/OpenInterpreter/01", "href": "https://github.com/OpenInterpreter/01", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "LLaVA-NeXT is recently merged to Hugging Face transformers and it outperforms many of the closed source models like Gemini on various benchmarks 🤩 Let's take a look!", "raw": "LLaVA-NeXT is recently merged to Hugging Face transformers and it outperforms many of the closed source models like Gemini on various benchmarks 🤩 Let's take a look!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/merve/llava-next", "href": null, "resource": { "type": "space", "id": "merve/llava-next", "discussionNum": null }, "url": "https://huggingface.co/spaces/merve/llava-next", "code": null, "user": null, "label": null, "lang": null }, { "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/1afNudu72SNWZCYtCVrRlb9T9Vj9CFJEK?usp=sharing", "href": "https://colab.research.google.com/drive/1afNudu72SNWZCYtCVrRlb9T9Vj9CFJEK?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": "LLaVA is essentially a vision-language model that consists of ViT-based CLIP encoder, a MLP projection and Vicuna as decoder ✨", "raw": "LLaVA is essentially a vision-language model that consists of ViT-based CLIP encoder, a MLP projection and Vicuna as decoder ✨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "LLaVA 1.5 was released with Vicuna, but LLaVA NeXT (1.6) is released with four different LLMs:", "raw": "LLaVA 1.5 was released with Vicuna, but LLaVA NeXT (1.6) is released with four different 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": "text", "value": "- Nous-Hermes-Yi-34B", "raw": "- Nous-Hermes-Yi-34B", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Mistral-7B", "raw": "- Mistral-7B", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Vicuna 7B & 13B ", "raw": "- Vicuna 7B & 13B ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Mistral and Nous-Hermes-Yi-34B are performing better and have better commercial use. ", "raw": "Mistral and Nous-Hermes-Yi-34B are performing better and have better commercial 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": "Moreover, according to authors' findings, the improvements comes from more diverse and high quality data mixture and dynamic high resolution.", "raw": "Moreover, according to authors' findings, the improvements comes from more diverse and high quality data mixture and dynamic high resolution.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "LLaVA based on Nous-Hermes-Yi-34B outperforms many other models, including Gemini in various multimodal understanding and generation benchmarks 😊 ", "raw": "LLaVA based on Nous-Hermes-Yi-34B outperforms many other models, including Gemini in various multimodal understanding and generation benchmarks 😊 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "New NVIDIA GTC24 paper 🎊", "raw": "New NVIDIA GTC24 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": "We generate high-quality 3D assets in only 400ms from text by combining (a) amortized optimization for speed, (b) surface rendering for quality, and (c) 3D data for robustness.", "raw": "We generate high-quality 3D assets in only 400ms from text by combining (a) amortized optimization for speed, (b) surface rendering for quality, and (c) 3D data for robustness.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "☕ LATTE3D project details: ", "raw": "☕ LATTE3D project details: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://research.nvidia.com/labs/toronto-ai/LATTE3D/", "href": "https://research.nvidia.com/labs/toronto-ai/LATTE3D/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🌊LaVague can compile Action Plans into actionable code to browse the internet!", "raw": "🌊LaVague can compile Action Plans into actionable code to browse 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": "In this example, you can see how an action plan with natural language instructions can be “compiled” into executable Selenium code!", "raw": "In this example, you can see how an action plan with natural language instructions can be “compiled” into executable Selenium code!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 shows the potential of #LAM (Large Action Models) to perform actions for us and automate mechanical tasks. ", "raw": "🤖This shows the potential of #LAM (Large Action Models) to perform actions for us and automate mechanical 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": "This example leverages a local embedding model and OpenAI GPT-3.5, but we support many options, including local ones with Gemma!", "raw": "This example leverages a local embedding model and OpenAI GPT-3.5, but we support many options, including local ones with Gemma!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 this in our docs: ", "raw": "You can try this in our docs: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://docs.lavague.ai/en/latest/", "href": "https://docs.lavague.ai/en/latest/", "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": "LaVague is an open-source Large Action Model framework to automate automation. If you are interested in helping us on our mission to democratize automation tooling for devs, don’t hesitate to visit our GitHub (", "raw": "LaVague is an open-source Large Action Model framework to automate automation. If you are interested in helping us on our mission to democratize automation tooling for devs, don’t hesitate to visit our GitHub (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/lavague-ai/LaVague", "href": "https://github.com/lavague-ai/LaVague", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") or Discord (", "raw": ") or Discord (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://discord.gg/SDxn9KpqX9", "href": "https://discord.gg/SDxn9KpqX9", "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": "Mora", "raw": "Mora", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Enabling Generalist Video Generation via A Multi-Agent Framework", "raw": "Enabling Generalist Video Generation via A Multi-Agent Framework", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "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/2403.13248", "href": null, "resource": { "type": "paper", "id": "2403.13248", "discussionNum": null }, "url": "https://huggingface.co/papers/2403.13248", "code": null, "user": null, "label": "Mora: Enabling Generalist Video Generation via A Multi-Agent Framework (2403.13248)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Sora is the first large-scale generalist video generation model that garnered significant attention across society. Since its launch by OpenAI in February 2024, no other video generation models have paralleled {Sora}'s performance or its capacity to support a broad spectrum of video generation tasks. Additionally, there are only a few fully published video generation models, with the majority being closed-source. To address this gap, this paper proposes a new multi-agent framework Mora, which incorporates several advanced visual AI agents to replicate generalist video generation demonstrated by Sora. In particular, Mora can utilize multiple visual agents and successfully mimic Sora's video generation capabilities in various tasks, such as (1) text-to-video generation, (2) text-conditional image-to-video generation, (3) extend generated videos, (4) video-to-video editing, (5) connect videos and (6) simulate digital worlds. Our extensive experimental results show that Mora achieves performance that is proximate to that of Sora in various tasks. However, there exists an obvious performance gap between our work and Sora when assessed holistically. In summary, we hope this project can guide the future trajectory of video generation through collaborative AI agents.", "raw": "Sora is the first large-scale generalist video generation model that garnered significant attention across society. Since its launch by OpenAI in February 2024, no other video generation models have paralleled {Sora}'s performance or its capacity to support a broad spectrum of video generation tasks. Additionally, there are only a few fully published video generation models, with the majority being closed-source. To address this gap, this paper proposes a new multi-agent framework Mora, which incorporates several advanced visual AI agents to replicate generalist video generation demonstrated by Sora. In particular, Mora can utilize multiple visual agents and successfully mimic Sora's video generation capabilities in various tasks, such as (1) text-to-video generation, (2) text-conditional image-to-video generation, (3) extend generated videos, (4) video-to-video editing, (5) connect videos and (6) simulate digital worlds. Our extensive experimental results show that Mora achieves performance that is proximate to that of Sora in various tasks. However, there exists an obvious performance gap between our work and Sora when assessed holistically. In summary, we hope this project can guide the future trajectory of video generation through collaborative AI agents.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": 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": "How talkative is your chatbot about your internal data? 😬", "raw": "How talkative is your chatbot about your internal 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": "As more chatbots get deployed in production, with access to internal databases, we need to make sure they don't leak private information to anyone interacting with them.", "raw": "As more chatbots get deployed in production, with access to internal databases, we need to make sure they don't leak private information to anyone interacting with 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": "The Lighthouz AI team therefore introduced the Chatbot Guardrails Arena to stress test models and see how well guarded your private information is.", "raw": "The Lighthouz AI team therefore introduced the Chatbot Guardrails Arena to stress test models and see how well guarded your private information 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": "text", "value": "Anyone can try to make models reveal information they should not share 😈", "raw": "Anyone can try to make models reveal information they should not share 😈", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "(which is quite fun to do for the strongest models)!", "raw": "(which is quite fun to do for the strongest 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": "The votes will then be gathered to create an Elo ranking of the safest models with respect to PII. ", "raw": "The votes will then be gathered to create an Elo ranking of the safest models with respect to PII. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 the future, with the support of the community, this arena could inform safety choices that company make, when choosing models and guardrails on their resistance to adversarial attacks.", "raw": "In the future, with the support of the community, this arena could inform safety choices that company make, when choosing models and guardrails on their resistance to adversarial attacks.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 a good way to easily demonstrate the limitations of current systems!", "raw": "It's also a good way to easily demonstrate the limitations of current systems!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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 arena: ", "raw": "Check out the arena: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/lighthouzai/guardrails-arena", "href": null, "resource": { "type": "space", "id": "lighthouzai/guardrails-arena", "discussionNum": null }, "url": "https://huggingface.co/spaces/lighthouzai/guardrails-arena", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Learn more in the blog: ", "raw": "Learn more in the blog: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/arena-lighthouz", "href": "https://huggingface.co/blog/arena-lighthouz", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]

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[ { "type": "text", "value": "🚀🎭🌟 New Research Alert - CVPR 2024! 🌟 🎭🚀", "raw": "🚀🎭🌟 New Research Alert - CVPR 2024! 🌟 🎭🚀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Title: Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians 🔝", "raw": "📄 Title: Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians 🔝", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📝 Description: Gaussian Head Avatar is a method for generating highly detailed 3D head avatars using dynamic Gaussian functions controlled by a neural network, ensuring ultra-high quality visualization even under limited viewpoints.", "raw": "📝 Description: Gaussian Head Avatar is a method for generating highly detailed 3D head avatars using dynamic Gaussian functions controlled by a neural network, ensuring ultra-high quality visualization even under limited viewpoints.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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: Yuelang Xu, ", "raw": "👥 Authors: Yuelang Xu, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@ben55", "href": null, "resource": null, "url": null, "code": null, "user": "ben55", "label": null, "lang": null }, { "type": "text", "value": ", Zhe Li, ", "raw": ", Zhe Li, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@HongwenZhang", "href": null, "resource": null, "url": null, "code": null, "user": "HongwenZhang", "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": "@wanglz14", "href": null, "resource": null, "url": null, "code": null, "user": "wanglz14", "label": null, "lang": null }, { "type": "text", "value": ", Zerong Zheng, and ", "raw": ", Zerong Zheng, and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@YebinLiu", "href": null, "resource": null, "url": null, "code": null, "user": "YebinLiu", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📅 Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA 🇺🇸", "raw": "📅 Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA 🇺🇸", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "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/2312.03029", "href": null, "resource": { "type": "paper", "id": "2312.03029", "discussionNum": null }, "url": "https://huggingface.co/papers/2312.03029", "code": null, "user": null, "label": "Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic\n Gaussians (2312.03029)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": 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