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One new repository is created every 15 seconds. Congratulations all!","raw":"We just crossed 1,500,000 public models on Hugging Face (and 500k spaces, 330k datasets, 50k papers). One new repository is created every 15 seconds. Congratulations all!"}],"rawContent":"We just crossed 1,500,000 public models on Hugging Face (and 500k spaces, 330k datasets, 50k papers). One new repository is created every 15 seconds. Congratulations all!","author":{"_id":"5e67bdd61009063689407479","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1583857146757-5e67bdd61009063689407479.jpeg","fullname":"Clem 🤗","name":"clem","type":"user","isPro":true,"isHf":true,"isMod":false,"followerCount":2130,"isFollowing":false},"attachments":[{"type":"image","url":"https://cdn-uploads.huggingface.co/production/uploads/5e67bdd61009063689407479/130LXNSCuxU5-BYCfRiOx.png"}],"mentions":[],"reactions":[{"reaction":"🤗","users":["John6666","LihiShalmon","alielfilali01","daniel-ltw","Tomcs","Vetzooccess","not-lain","robb-0","J4BEZ"],"count":9},{"reaction":"🚀","users":["monsoon-nlp","fdaudens","csabakecskemeti","not-lain"],"count":4}],"publishedAt":"2025-03-13T18:18:48.000Z","updatedAt":"2025-03-13T21:05:37.560Z","commentators":[{"_id":"600f54515b3424eba81d28f5","avatarUrl":"/avatars/62a28a21320a05cc7afa27ee1f8fd465.svg","fullname":"Tom Sta","name":"Tomcs","type":"user","isPro":true,"isHf":false,"isMod":false,"followerCount":3,"isFollowing":false}],"url":"/posts/clem/238420842235482","totalUniqueImpressions":377,"identifiedLanguage":{"language":"en","probability":0.8790357708930969},"numComments":1}},{"time":"2025-03-13T15:37:33.261Z","user":"Wauplin","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1659336880158-6273f303f6d63a28483fde12.png","org":"huggingface","orgAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1583856921041-5dd96eb166059660ed1ee413.png","type":"discussion","discussionData":{"num":14,"author":{"_id":"5f17f0a0925b9863e28ad517","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/5f17f0a0925b9863e28ad517/fXIY5i9RLsIa1v3CCuVtt.jpeg","fullname":"Victor Mustar","name":"victor","type":"user","isPro":true,"isHf":true,"isMod":false,"followerCount":3217},"repo":{"name":"huggingface/HuggingDiscussions","type":"space"},"title":"[FEEDBACK] Follow","status":"open","createdAt":"2023-10-24T12:41:12.000Z","isPullRequest":false,"numComments":13,"pinned":true,"repoOwner":{"name":"huggingface","isParticipating":true,"type":"org","isDiscussionAuthor":true}},"repoId":"huggingface/HuggingDiscussions","repoType":"space","eventId":"67d2fbbdeb87c33368480f5d"},{"time":"2025-03-13T15:05:23.342Z","user":"burtenshaw","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62d648291fa3e4e7ae3fa6e8/oatOwf8Xqe5eDbCSuYqCd.png","type":"social-post","socialPost":{"slug":"988620578150041","content":[{"type":"text","value":"Still speed running Gemma 3 to think. Today I focused on setting up gpu poor hardware to run GRPO. ","raw":"Still speed running Gemma 3 to think. Today I focused on setting up gpu poor hardware to run GRPO. "},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"This is a plain TRL and PEFT notebook which works on mac silicone or colab T4. This uses the 1b variant of Gemma 3 and a reasoning version of GSM8K dataset.","raw":"This is a plain TRL and PEFT notebook which works on mac silicone or colab T4. This uses the 1b variant of Gemma 3 and a reasoning version of GSM8K dataset."},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"🧑‍🍳 There’s more still in the oven like releasing models, an Unsloth version, and deeper tutorials, but hopefully this should bootstrap your projects.","raw":"🧑‍🍳 There’s more still in the oven like releasing models, an Unsloth version, and deeper tutorials, but hopefully this should bootstrap your projects."},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Here’s a link to the 1b notebook: ","raw":"Here’s a link to the 1b notebook: "},{"type":"link","href":"https://colab.research.google.com/drive/1mwCy5GQb9xJFSuwt2L_We3eKkVbx2qSt?usp=sharing","raw":"https://colab.research.google.com/drive/1mwCy5GQb9xJFSuwt2L_We3eKkVbx2qSt?usp=sharing"},{"type":"new_line","raw":"\n"}],"rawContent":"Still speed running Gemma 3 to think. Today I focused on setting up gpu poor hardware to run GRPO. \n\nThis is a plain TRL and PEFT notebook which works on mac silicone or colab T4. This uses the 1b variant of Gemma 3 and a reasoning version of GSM8K dataset.\n\n🧑‍🍳 There’s more still in the oven like releasing models, an Unsloth version, and deeper tutorials, but hopefully this should bootstrap your projects.\n\nHere’s a link to the 1b notebook: https://colab.research.google.com/drive/1mwCy5GQb9xJFSuwt2L_We3eKkVbx2qSt?usp=sharing\n","author":{"_id":"62d648291fa3e4e7ae3fa6e8","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62d648291fa3e4e7ae3fa6e8/oatOwf8Xqe5eDbCSuYqCd.png","fullname":"ben burtenshaw","name":"burtenshaw","type":"user","isPro":false,"isHf":true,"isMod":false,"followerCount":1906,"isFollowing":false},"attachments":[{"type":"image","url":"https://cdn-uploads.huggingface.co/production/uploads/62d648291fa3e4e7ae3fa6e8/w7oDOan17Hk3ClIdnmPkc.png"}],"mentions":[],"reactions":[{"reaction":"👍","users":["John6666","frank0757"],"count":2},{"reaction":"🔥","users":["salym"],"count":1}],"publishedAt":"2025-03-13T15:05:23.000Z","updatedAt":"2025-03-13T15:05:23.292Z","commentators":[],"url":"/posts/burtenshaw/988620578150041","totalUniqueImpressions":352,"identifiedLanguage":{"language":"en","probability":0.8180099725723267},"numComments":0}},{"time":"2025-03-13T09:17:47.550Z","user":"burtenshaw","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62d648291fa3e4e7ae3fa6e8/oatOwf8Xqe5eDbCSuYqCd.png","type":"social-post","socialPost":{"slug":"224487224687488","content":[{"type":"text","value":"everybody and their dog is fine-tuning Gemma 3 today, so I thought I'd do a longer post on the tips and sharp edges I find. let's go!","raw":"everybody and their dog is fine-tuning Gemma 3 today, so I thought I'd do a longer post on the tips and sharp edges I find. let's go!"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"1. has to be install everything form main and nightly. this is what I'm working with to get unsloth and TRL running","raw":"1. has to be install everything form main and nightly. this is what I'm working with to get unsloth and TRL running"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"code_fence","lang":"txt","code":"git+https://github.com/huggingface/transformers@main\ngit+https://github.com/huggingface/trl.git@main\nbitsandbytes\npeft","raw":"```txt\ngit+https://github.com/huggingface/transformers@main\ngit+https://github.com/huggingface/trl.git@main\nbitsandbytes\npeft\n```"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"plus this with ","raw":"plus this with "},{"type":"inline_code","code":"--no-deps","raw":"`--no-deps`"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"code_fence","lang":"txt","code":"git+https://github.com/unslothai/unsloth-zoo.git@nightly\ngit+https://github.com/unslothai/unsloth.git@nightly","raw":"```txt\ngit+https://github.com/unslothai/unsloth-zoo.git@nightly\ngit+https://github.com/unslothai/unsloth.git@nightly\n```"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"2. will brown's code to turn GSM8k into a reasoning dataset is a nice toy experiment ","raw":"2. will brown's code to turn GSM8k into a reasoning dataset is a nice toy experiment "},{"type":"link","href":"https://gist.github.com/willccbb/4676755236bb08cab5f4e54a0475d6fb","raw":"https://gist.github.com/willccbb/4676755236bb08cab5f4e54a0475d6fb"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"3. with a learning rate of 5e-6 rewards and loss stayed flat for the first 100 or so steps.","raw":"3. with a learning rate of 5e-6 rewards and loss stayed flat for the first 100 or so steps."},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"4. so far none of my runs have undermined the outputs after 1 epoch. therefore, I'm mainly experimenting with bigger LoRA adapters.","raw":"4. so far none of my runs have undermined the outputs after 1 epoch. therefore, I'm mainly experimenting with bigger LoRA adapters."},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"code_fence","lang":"python","code":"from trl import GRPOConfig\n\ntraining_args = GRPOConfig(\n learning_rate = 5e-6,\n adam_beta1 = 0.9,\n adam_beta2 = 0.99,\n weight_decay = 0.1,\n warmup_ratio = 0.1,\n lr_scheduler_type = \"cosine\",\n optim = \"adamw_8bit\",\n logging_steps = 1,\n per_device_train_batch_size = 2,\n gradient_accumulation_steps = 1,\n num_generations = 2,\n max_prompt_length = 256,\n max_completion_length = 1024 - 256,\n num_train_epochs = 1,\n max_steps = 250,\n save_steps = 250,\n max_grad_norm = 0.1,\n report_to = \"none\",\n)","raw":"```python\nfrom trl import GRPOConfig\n\ntraining_args = GRPOConfig(\n learning_rate = 5e-6,\n adam_beta1 = 0.9,\n adam_beta2 = 0.99,\n weight_decay = 0.1,\n warmup_ratio = 0.1,\n lr_scheduler_type = \"cosine\",\n optim = \"adamw_8bit\",\n logging_steps = 1,\n per_device_train_batch_size = 2,\n gradient_accumulation_steps = 1,\n num_generations = 2,\n max_prompt_length = 256,\n max_completion_length = 1024 - 256,\n num_train_epochs = 1,\n max_steps = 250,\n save_steps = 250,\n max_grad_norm = 0.1,\n report_to = \"none\",\n)\n```"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"5. vision fine-tuning isn't available in TRL's GRPOTrainer, so stick to text datasets. but no need to load the model differently in transformers or Unsloth","raw":"5. vision fine-tuning isn't available in TRL's GRPOTrainer, so stick to text datasets. but no need to load the model differently in transformers or Unsloth"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"code_fence","lang":"python","code":"from transformers import AutoModelForImageTextToText\n\nmodel = AutoModelForImageTextToText.from_pretrained(\"google/gemma-3-4b-it)","raw":"```python\nfrom transformers import AutoModelForImageTextToText\n\nmodel = AutoModelForImageTextToText.from_pretrained(\"google/gemma-3-4b-it)\n```"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"if you want an introduction to GRPO, check out the reasoning course, it walks you through the algorithm, theory, and implementation in a smooth way.","raw":"if you want an introduction to GRPO, check out the reasoning course, it walks you through the algorithm, theory, and implementation in a smooth way."},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"link","href":"https://huggingface.co/reasoning-course","raw":"https://huggingface.co/reasoning-course"}],"rawContent":"everybody and their dog is fine-tuning Gemma 3 today, so I thought I'd do a longer post on the tips and sharp edges I find. let's go!\n\n1. has to be install everything form main and nightly. this is what I'm working with to get unsloth and TRL running\n\n```txt\ngit+https://github.com/huggingface/transformers@main\ngit+https://github.com/huggingface/trl.git@main\nbitsandbytes\npeft\n```\n\nplus this with `--no-deps`\n\n```txt\ngit+https://github.com/unslothai/unsloth-zoo.git@nightly\ngit+https://github.com/unslothai/unsloth.git@nightly\n```\n\n2. will brown's code to turn GSM8k into a reasoning dataset is a nice toy experiment https://gist.github.com/willccbb/4676755236bb08cab5f4e54a0475d6fb\n\n3. with a learning rate of 5e-6 rewards and loss stayed flat for the first 100 or so steps.\n\n4. so far none of my runs have undermined the outputs after 1 epoch. therefore, I'm mainly experimenting with bigger LoRA adapters.\n\n```python\nfrom trl import GRPOConfig\n\ntraining_args = GRPOConfig(\n learning_rate = 5e-6,\n adam_beta1 = 0.9,\n adam_beta2 = 0.99,\n weight_decay = 0.1,\n warmup_ratio = 0.1,\n lr_scheduler_type = \"cosine\",\n optim = \"adamw_8bit\",\n logging_steps = 1,\n per_device_train_batch_size = 2,\n gradient_accumulation_steps = 1,\n num_generations = 2,\n max_prompt_length = 256,\n max_completion_length = 1024 - 256,\n num_train_epochs = 1,\n max_steps = 250,\n save_steps = 250,\n max_grad_norm = 0.1,\n report_to = \"none\",\n)\n```\n\n5. vision fine-tuning isn't available in TRL's GRPOTrainer, so stick to text datasets. but no need to load the model differently in transformers or Unsloth\n\n```python\nfrom transformers import AutoModelForImageTextToText\n\nmodel = AutoModelForImageTextToText.from_pretrained(\"google/gemma-3-4b-it)\n```\n\nif you want an introduction to GRPO, check out the reasoning course, it walks you through the algorithm, theory, and implementation in a smooth way.\n\nhttps://huggingface.co/reasoning-course","author":{"_id":"62d648291fa3e4e7ae3fa6e8","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62d648291fa3e4e7ae3fa6e8/oatOwf8Xqe5eDbCSuYqCd.png","fullname":"ben burtenshaw","name":"burtenshaw","type":"user","isPro":false,"isHf":true,"isMod":false,"followerCount":1906,"isFollowing":false},"attachments":[],"mentions":[],"reactions":[{"reaction":"🤗","users":["KingNish","mshojaei77","John6666"],"count":3},{"reaction":"🔥","users":["BrigitteTousi"],"count":1}],"publishedAt":"2025-03-13T09:17:47.000Z","updatedAt":"2025-03-13T13:53:30.981Z","commentators":[{"_id":"62d648291fa3e4e7ae3fa6e8","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62d648291fa3e4e7ae3fa6e8/oatOwf8Xqe5eDbCSuYqCd.png","fullname":"ben burtenshaw","name":"burtenshaw","type":"user","isPro":false,"isHf":true,"isMod":false,"followerCount":1906,"isFollowing":false},{"_id":"63691c3eda9b693c2730b2a2","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63691c3eda9b693c2730b2a2/hBtKpgo3_9003MWCGkw5d.png","fullname":"Brigitte Tousignant","name":"BrigitteTousi","type":"user","isPro":false,"isHf":true,"isMod":false,"followerCount":204,"isFollowing":false}],"url":"/posts/burtenshaw/224487224687488","totalUniqueImpressions":720,"identifiedLanguage":{"language":"en","probability":0.798825204372406},"numComments":2}},{"time":"2025-03-12T19:29:55.509Z","user":"a-r-r-o-w","userAvatarUrl":"/avatars/ce9b99882a65fd2cb983ba71a5ac2473.svg","org":"huggingface","orgAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1583856921041-5dd96eb166059660ed1ee413.png","type":"discussion","discussionData":{"num":450,"author":{"_id":"6484c61300f3d63d6c3d6b27","avatarUrl":"/avatars/ce9b99882a65fd2cb983ba71a5ac2473.svg","fullname":"Aryan V S","name":"a-r-r-o-w","type":"user","isPro":false,"isHf":true,"isMod":false,"followerCount":56},"repo":{"name":"huggingface/documentation-images","type":"dataset"},"title":"Upload folder using huggingface_hub","status":"merged","createdAt":"2025-03-12T19:29:16.000Z","isPullRequest":true,"numComments":2,"pinned":false,"repoOwner":{"name":"huggingface","isParticipating":true,"type":"org","isDiscussionAuthor":true}},"repoId":"huggingface/documentation-images","repoType":"dataset","eventId":"67d1e0b3f2930bc96934f16b"},{"time":"2025-03-12T17:29:47.832Z","user":"abidlabs","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1621947938344-noauth.png","orgAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1583856921041-5dd96eb166059660ed1ee413.png","type":"update","repoData":{"author":"huggingface","downloads":4938550,"gated":false,"id":"huggingface/documentation-images","lastModified":"2025-03-12T19:29:55.000Z","datasetsServerInfo":{"viewer":"viewer","numRows":50,"libraries":["datasets","mlcroissant"],"formats":["imagefolder"],"modalities":["image"]},"private":false,"repoType":"dataset","likes":53,"isLikedByUser":false},"repoId":"huggingface/documentation-images","repoType":"dataset","org":"huggingface"},{"time":"2025-03-12T16:04:26.390Z","user":"burtenshaw","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62d648291fa3e4e7ae3fa6e8/oatOwf8Xqe5eDbCSuYqCd.png","type":"social-post","socialPost":{"slug":"548205593122339","content":[{"type":"text","value":"Here’s a notebook to make Gemma reason with GRPO & TRL. I made this whilst prepping the next unit of the reasoning course:","raw":"Here’s a notebook to make Gemma reason with GRPO & TRL. I made this whilst prepping the next unit of the reasoning course:"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"In this notebooks I combine together google’s model with some community tooling","raw":"In this notebooks I combine together google’s model with some community tooling"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"- First, I load the model from the Hugging Face hub with transformers’s latest release for Gemma 3","raw":"- First, I load the model from the Hugging Face hub with transformers’s latest release for Gemma 3"},{"type":"new_line","raw":"\n"},{"type":"text","value":"- I use PEFT and bitsandbytes to get it running on Colab","raw":"- I use PEFT and bitsandbytes to get it running on Colab"},{"type":"new_line","raw":"\n"},{"type":"text","value":"- Then, I took Will Browns processing and reward functions to make reasoning chains from GSM8k","raw":"- Then, I took Will Browns processing and reward functions to make reasoning chains from GSM8k"},{"type":"new_line","raw":"\n"},{"type":"text","value":"- Finally, I used TRL’s GRPOTrainer to train the model","raw":"- Finally, I used TRL’s GRPOTrainer to train the model"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Next step is to bring Unsloth AI in, then ship it in the reasoning course. Links to notebook below.","raw":"Next step is to bring Unsloth AI in, then ship it in the reasoning course. Links to notebook below."},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"link","href":"https://colab.research.google.com/drive/1Vkl69ytCS3bvOtV9_stRETMthlQXR4wX?usp=sharing","raw":"https://colab.research.google.com/drive/1Vkl69ytCS3bvOtV9_stRETMthlQXR4wX?usp=sharing"}],"rawContent":"Here’s a notebook to make Gemma reason with GRPO & TRL. I made this whilst prepping the next unit of the reasoning course:\n\nIn this notebooks I combine together google’s model with some community tooling\n\n- First, I load the model from the Hugging Face hub with transformers’s latest release for Gemma 3\n- I use PEFT and bitsandbytes to get it running on Colab\n- Then, I took Will Browns processing and reward functions to make reasoning chains from GSM8k\n- Finally, I used TRL’s GRPOTrainer to train the model\n\nNext step is to bring Unsloth AI in, then ship it in the reasoning course. Links to notebook below.\n\nhttps://colab.research.google.com/drive/1Vkl69ytCS3bvOtV9_stRETMthlQXR4wX?usp=sharing","author":{"_id":"62d648291fa3e4e7ae3fa6e8","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62d648291fa3e4e7ae3fa6e8/oatOwf8Xqe5eDbCSuYqCd.png","fullname":"ben burtenshaw","name":"burtenshaw","type":"user","isPro":false,"isHf":true,"isMod":false,"followerCount":1906,"isFollowing":false},"attachments":[],"mentions":[],"reactions":[{"reaction":"👍","users":["AGenchev","ritvik77","AtAndDev","sugatoray","jack729"],"count":5},{"reaction":"🤗","users":["John6666","AtAndDev","ZennyKenny"],"count":3},{"reaction":"❤️","users":["salym"],"count":1}],"publishedAt":"2025-03-12T16:04:26.000Z","updatedAt":"2025-03-13T23:14:38.089Z","commentators":[{"_id":"630f3e4002ce39336c411048","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/630f3e4002ce39336c411048/Wlldb-4EU5poO6JYmZp2I.png","fullname":"alkinun","name":"AtAndDev","type":"user","isPro":false,"isHf":false,"isMod":false,"followerCount":25,"isFollowing":false},{"_id":"676c67da7bad1587f2d046e2","avatarUrl":"/avatars/39d8ca8c5d14fdd4a704a29b73606e1e.svg","fullname":"Akhil Theerthala","name":"Akhil-Theerthala","type":"user","isPro":false,"isHf":false,"isMod":false,"isFollowing":false},{"_id":"656e3808d4de03a07d116850","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/656e3808d4de03a07d116850/62cFw46AmuhdI3gS24F1M.jpeg","fullname":"Kenneth Hamilton","name":"ZennyKenny","type":"user","isPro":true,"isHf":false,"isMod":false,"followerCount":56,"isFollowing":false}],"url":"/posts/burtenshaw/548205593122339","totalUniqueImpressions":1055,"identifiedLanguage":{"language":"en","probability":0.8596261739730835},"numComments":3}},{"time":"2025-03-12T15:53:47.890Z","user":"lewtun","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1594651707950-noauth.jpeg","type":"paper","paper":{"id":"2503.07572","title":"Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning","publishedAt":"2025-03-10T17:40:43.000Z","upvotes":31,"isUpvotedByUser":true}},{"time":"2025-03-12T13:16:09.318Z","user":"enzostvs","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64f73f25098581ab15e2f5ad/gyBjr8ea-wrpXWzJEl_-9.png","orgAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1583856921041-5dd96eb166059660ed1ee413.png","type":"update","repoData":{"author":"huggingface","downloads":4938550,"gated":false,"id":"huggingface/documentation-images","lastModified":"2025-03-12T19:29:55.000Z","datasetsServerInfo":{"viewer":"viewer","numRows":50,"libraries":["datasets","mlcroissant"],"formats":["imagefolder"],"modalities":["image"]},"private":false,"repoType":"dataset","likes":53,"isLikedByUser":false},"repoId":"huggingface/documentation-images","repoType":"dataset","org":"huggingface"},{"time":"2025-03-12T08:08:59.746Z","user":"lysandre","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/5e3aec01f55e2b62848a5217/PMKS0NNB4MJQlTSFzh918.jpeg","orgAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1583856921041-5dd96eb166059660ed1ee413.png","type":"update","repoData":{"author":"huggingface","downloads":5052,"gated":false,"id":"huggingface/transformers-metadata","lastModified":"2025-03-12T08:08:57.000Z","datasetsServerInfo":{"viewer":"viewer","numRows":1577,"libraries":["datasets","pandas","mlcroissant","polars"],"formats":["json"],"modalities":["text"]},"private":false,"repoType":"dataset","likes":16,"isLikedByUser":false},"repoId":"huggingface/transformers-metadata","repoType":"dataset","org":"huggingface"},{"time":"2025-03-12T07:58:02.145Z","user":"clefourrier","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1644340617257-noauth.png","type":"social-post","socialPost":{"slug":"544255220051399","content":[{"type":"text","value":"Gemma3 family is out! Reading the tech report, and this section was really interesting to me from a methods/scientific fairness pov.","raw":"Gemma3 family is out! Reading the tech report, and this section was really interesting to me from a methods/scientific fairness pov."},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Instead of doing over-hyped comparisons, they clearly state that **results are reported in a setup which is advantageous to their models**.","raw":"Instead of doing over-hyped comparisons, they clearly state that **results are reported in a setup which is advantageous to their models**."},{"type":"new_line","raw":"\n"},{"type":"text","value":"(Which everybody does, but people usually don't say)","raw":"(Which everybody does, but people usually don't say)"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"For a tech report, it makes a lot of sense to report model performance when used optimally!","raw":"For a tech report, it makes a lot of sense to report model performance when used optimally!"},{"type":"new_line","raw":"\n"},{"type":"text","value":"On leaderboards on the other hand, comparison will be apples to apples, but in a potentially unoptimal way for a given model family (like some user interact sub-optimally with models)","raw":"On leaderboards on the other hand, comparison will be apples to apples, but in a potentially unoptimal way for a given model family (like some user interact sub-optimally with models)"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Also contains a cool section (6) on training data memorization rate too! Important to see if your model will output the training data it has seen as such: always an issue for privacy/copyright/... but also very much for evaluation! ","raw":"Also contains a cool section (6) on training data memorization rate too! Important to see if your model will output the training data it has seen as such: always an issue for privacy/copyright/... but also very much for evaluation! "},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Because if your model knows its evals by heart, you're not testing for generalization.","raw":"Because if your model knows its evals by heart, you're not testing for generalization."}],"rawContent":"Gemma3 family is out! Reading the tech report, and this section was really interesting to me from a methods/scientific fairness pov.\n\nInstead of doing over-hyped comparisons, they clearly state that **results are reported in a setup which is advantageous to their models**.\n(Which everybody does, but people usually don't say)\n\nFor a tech report, it makes a lot of sense to report model performance when used optimally!\nOn leaderboards on the other hand, comparison will be apples to apples, but in a potentially unoptimal way for a given model family (like some user interact sub-optimally with models)\n\nAlso contains a cool section (6) on training data memorization rate too! Important to see if your model will output the training data it has seen as such: always an issue for privacy/copyright/... but also very much for evaluation! \n\nBecause if your model knows its evals by heart, you're not testing for generalization.","author":{"_id":"6202a599216215a22221dea9","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1644340617257-noauth.png","fullname":"Clémentine Fourrier","name":"clefourrier","type":"user","isPro":false,"isHf":true,"isMod":false,"followerCount":565,"isFollowing":false},"attachments":[{"type":"image","url":"https://cdn-uploads.huggingface.co/production/uploads/6202a599216215a22221dea9/7h3eGEkrg401EnNl32VFC.png"}],"mentions":[],"reactions":[{"reaction":"🚀","users":["John6666","BrigitteTousi","AdinaY","Makar7","AtAndDev"],"count":5},{"reaction":"👍","users":["jiminHuang","SeedOfEvil","AtAndDev"],"count":3}],"publishedAt":"2025-03-12T07:58:02.000Z","updatedAt":"2025-03-12T08:05:53.790Z","commentators":[],"url":"/posts/clefourrier/544255220051399","totalUniqueImpressions":1307,"identifiedLanguage":{"language":"en","probability":0.9575662016868591},"numComments":0}},{"time":"2025-03-12T07:12:52.340Z","user":"ariG23498","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/608aabf24955d2bfc3cd99c6/T762Ut0Y-w0sZB2ynvfbJ.jpeg","orgAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1583856921041-5dd96eb166059660ed1ee413.png","type":"update","repoData":{"author":"huggingface","downloads":4938550,"gated":false,"id":"huggingface/documentation-images","lastModified":"2025-03-12T19:29:55.000Z","datasetsServerInfo":{"viewer":"viewer","numRows":50,"libraries":["datasets","mlcroissant"],"formats":["imagefolder"],"modalities":["image"]},"private":false,"repoType":"dataset","likes":53,"isLikedByUser":false},"repoId":"huggingface/documentation-images","repoType":"dataset","org":"huggingface"},{"time":"2025-03-11T21:52:30.825Z","user":"freddyaboulton","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1654278567459-626a9bfa03e2e2796f24ca11.jpeg","type":"social-post","socialPost":{"slug":"531889378584838","content":[{"type":"text","value":"Privacy matters when talking to AI! 🔇","raw":"Privacy matters when talking to AI! 🔇"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"We've just added a microphone mute button to FastRTC in our latest update (v0.0.14). Now you control exactly what your LLM hears.","raw":"We've just added a microphone mute button to FastRTC in our latest update (v0.0.14). Now you control exactly what your LLM hears."},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Plus lots more features in this release! Check them out:","raw":"Plus lots more features in this release! Check them out:"},{"type":"new_line","raw":"\n"},{"type":"link","href":"https://github.com/freddyaboulton/fastrtc/releases/tag/0.0.14","raw":"https://github.com/freddyaboulton/fastrtc/releases/tag/0.0.14"}],"rawContent":"Privacy matters when talking to AI! 🔇\n\nWe've just added a microphone mute button to FastRTC in our latest update (v0.0.14). Now you control exactly what your LLM hears.\n\nPlus lots more features in this release! Check them out:\nhttps://github.com/freddyaboulton/fastrtc/releases/tag/0.0.14","author":{"_id":"626a9bfa03e2e2796f24ca11","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1654278567459-626a9bfa03e2e2796f24ca11.jpeg","fullname":"Freddy Boulton","name":"freddyaboulton","type":"user","isPro":true,"isHf":true,"isMod":false,"followerCount":239,"isFollowing":false},"attachments":[{"type":"image","url":"https://cdn-uploads.huggingface.co/production/uploads/626a9bfa03e2e2796f24ca11/vCl_RixjU3xysnosKQlIe.gif"}],"mentions":[],"reactions":[{"reaction":"🧠","users":["Tonic","BrigitteTousi","John6666"],"count":3},{"reaction":"❤️","users":["Tonic","BrigitteTousi","tonalf"],"count":3},{"reaction":"🚀","users":["Tonic","BrigitteTousi","Fishtiks"],"count":3}],"publishedAt":"2025-03-11T21:52:30.000Z","updatedAt":"2025-03-11T21:52:30.720Z","commentators":[],"url":"/posts/freddyaboulton/531889378584838","totalUniqueImpressions":1631,"identifiedLanguage":{"language":"en","probability":0.8546785712242126},"numComments":0}},{"time":"2025-03-11T21:42:58.377Z","user":"lewtun","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1594651707950-noauth.jpeg","type":"social-post","socialPost":{"slug":"886287473065721","content":[{"type":"text","value":"Introducing OlympicCoder: a series of open reasoning models that can solve olympiad-level programming problems 🧑‍💻","raw":"Introducing OlympicCoder: a series of open reasoning models that can solve olympiad-level programming problems 🧑‍💻"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"- 7B ","raw":"- 7B "},{"type":"resource","resource":{"type":"model","id":"open-r1/OlympicCoder-7B"},"url":"https://huggingface.co/open-r1/OlympicCoder-7B","raw":"https://huggingface.co/open-r1/OlympicCoder-7B"},{"type":"new_line","raw":"\n"},{"type":"text","value":"- 32B ","raw":"- 32B "},{"type":"resource","resource":{"type":"model","id":"open-r1/OlympicCoder-32B"},"url":"https://huggingface.co/open-r1/OlympicCoder-32B","raw":"https://huggingface.co/open-r1/OlympicCoder-32B"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"We find that OlympicCoder models outperform Claude 3.7 Sonnet, as well as others over 100x larger 💪","raw":"We find that OlympicCoder models outperform Claude 3.7 Sonnet, as well as others over 100x larger 💪"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Together with the models, we are releasing: ","raw":"Together with the models, we are releasing: "},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"📊CodeForces-CoTs: new dataset of code problems from the most popular competitive coding platform, with R1 traces in C++ and Python ","raw":"📊CodeForces-CoTs: new dataset of code problems from the most popular competitive coding platform, with R1 traces in C++ and Python "},{"type":"resource","resource":{"type":"dataset","id":"open-r1/codeforces-cots"},"url":"https://huggingface.co/datasets/open-r1/codeforces-cots","raw":"https://huggingface.co/datasets/open-r1/codeforces-cots"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"🏆 IOI'2024: a new benchmark of VERY hard programming problems where even frontier models struggle to match human performance ","raw":"🏆 IOI'2024: a new benchmark of VERY hard programming problems where even frontier models struggle to match human performance "},{"type":"resource","resource":{"type":"dataset","id":"open-r1/ioi"},"url":"https://huggingface.co/datasets/open-r1/ioi","raw":"https://huggingface.co/datasets/open-r1/ioi"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"For links to the models and datasets, check out our latest progress report from Open R1: ","raw":"For links to the models and datasets, check out our latest progress report from Open R1: "},{"type":"link","href":"https://huggingface.co/blog/open-r1/update-3","raw":"https://huggingface.co/blog/open-r1/update-3"}],"rawContent":"Introducing OlympicCoder: a series of open reasoning models that can solve olympiad-level programming problems 🧑‍💻\n\n- 7B https://huggingface.co/open-r1/OlympicCoder-7B\n- 32B 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Built with ","raw":"If you ever asked which LLM is best for powering agents, we've just made a leaderboard that ranks them all! Built with "},{"type":"mention","user":"albertvillanova","raw":"@albertvillanova"},{"type":"text","value":", this ranks LLMs powering a smolagents CodeAgent on subsets of various benchmarks. ✅","raw":", this ranks LLMs powering a smolagents CodeAgent on subsets of various benchmarks. ✅"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"🏆 GPT-4.5 comes on top, even beating reasoning models like DeepSeek-R1 or o1. And Claude-3.7-Sonnet is a close second!","raw":"🏆 GPT-4.5 comes on top, even beating reasoning models like DeepSeek-R1 or o1. And Claude-3.7-Sonnet is a close second!"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"The leaderboard also allows you to show the scores of vanilla LLMs (without any agentic setup) on the same benchmarks: this shows the huge improvements brought by agentic setups. 💪","raw":"The leaderboard also allows you to show the scores of vanilla LLMs (without any agentic setup) on the same benchmarks: this shows the huge improvements brought by agentic setups. 💪"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"(Note that results will be added manually, so the leaderboard might not always have the latest LLMs)","raw":"(Note that results will be added manually, so the leaderboard might not always have the latest LLMs)"}],"rawContent":"Our new Agentic leaderboard is now live!💥\n\nIf you ever asked which LLM is best for powering agents, we've just made a leaderboard that ranks them all! Built with @albertvillanova, this ranks LLMs powering a smolagents CodeAgent on subsets of various benchmarks. ✅\n\n🏆 GPT-4.5 comes on top, even beating reasoning models like DeepSeek-R1 or o1. And Claude-3.7-Sonnet is a close second!\n\nThe leaderboard also allows you to show the scores of vanilla LLMs (without any agentic setup) on the same benchmarks: this shows the huge improvements brought by agentic setups. 💪\n\n(Note that results will be added manually, so the leaderboard might not always have the latest LLMs)","author":{"_id":"63d10d4e8eaa4831005e92b5","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63d10d4e8eaa4831005e92b5/7p7-OmWM6PqqCs7ZStPGD.jpeg","fullname":"Aymeric Roucher","name":"m-ric","type":"user","isPro":false,"isHf":true,"isMod":false,"followerCount":1316,"isFollowing":false},"attachments":[{"type":"image","url":"https://cdn-uploads.huggingface.co/production/uploads/63d10d4e8eaa4831005e92b5/BW4yJrtjdpqxgrjeZMEDC.png"}],"mentions":[{"_id":"5fbfd09ee366524fe8e97cd3","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1606406298765-noauth.jpeg","fullname":"Albert Villanova del Moral","name":"albertvillanova","type":"user","isPro":false,"isHf":true,"isMod":false,"followerCount":302}],"reactions":[{"reaction":"🤗","users":["John6666"],"count":1}],"publishedAt":"2025-03-10T18:53:36.000Z","updatedAt":"2025-03-11T03:26:55.499Z","commentators":[{"_id":"64b3eb9b93679cd0094dc95d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64b3eb9b93679cd0094dc95d/eBX-78aOs6cVencjM-3ME.png","fullname":"DataSoul","name":"DataSoul","type":"user","isPro":false,"isHf":false,"isMod":false,"followerCount":5,"isFollowing":false}],"url":"/posts/m-ric/960537980593987","totalUniqueImpressions":788,"identifiedLanguage":{"language":"en","probability":0.9106809496879578},"numComments":1}},{"time":"2025-03-10T10:06:28.029Z","user":"tomaarsen","userAvatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6317233cc92fd6fee317e030/cJHSvvimr1kqgQfHOjO5n.png","type":"social-post","socialPost":{"slug":"782540332014764","content":[{"type":"text","value":"An assembly of 18 European companies, labs, and universities have banded together to launch 🇪🇺 EuroBERT! It's a state-of-the-art multilingual encoder for 15 European languages, designed to be finetuned for retrieval, classification, etc. ","raw":"An assembly of 18 European companies, labs, and universities have banded together to launch 🇪🇺 EuroBERT! It's a state-of-the-art multilingual encoder for 15 European languages, designed to be finetuned for retrieval, classification, etc. "},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"🇪🇺 15 Languages: English, French, German, Spanish, Chinese, Italian, Russian, Polish, Portuguese, Japanese, Vietnamese, Dutch, Arabic, Turkish, Hindi","raw":"🇪🇺 15 Languages: English, French, German, Spanish, Chinese, Italian, Russian, Polish, Portuguese, Japanese, Vietnamese, Dutch, Arabic, Turkish, Hindi"},{"type":"new_line","raw":"\n"},{"type":"text","value":"3️⃣ 3 model sizes: 210M, 610M, and 2.1B parameters - very very useful sizes in my opinion","raw":"3️⃣ 3 model sizes: 210M, 610M, and 2.1B parameters - very very useful sizes in my opinion"},{"type":"new_line","raw":"\n"},{"type":"text","value":"➡️ Sequence length of 8192 tokens! Nice to see these higher sequence lengths for encoders becoming more common.","raw":"➡️ Sequence length of 8192 tokens! Nice to see these higher sequence lengths for encoders becoming more common."},{"type":"new_line","raw":"\n"},{"type":"text","value":"⚙️ Architecture based on Llama, but with bi-directional (non-causal) attention to turn it into an encoder. Flash Attention 2 is supported.","raw":"⚙️ Architecture based on Llama, but with bi-directional (non-causal) attention to turn it into an encoder. Flash Attention 2 is supported."},{"type":"new_line","raw":"\n"},{"type":"text","value":"🔥 A new Pareto frontier (stronger *and* smaller) for multilingual encoder models","raw":"🔥 A new Pareto frontier (stronger *and* smaller) for multilingual encoder models"},{"type":"new_line","raw":"\n"},{"type":"text","value":"📊 Evaluated against mDeBERTa, mGTE, XLM-RoBERTa for Retrieval, Classification, and Regression (after finetuning for each task separately): EuroBERT punches way above its weight.","raw":"📊 Evaluated against mDeBERTa, mGTE, XLM-RoBERTa for Retrieval, Classification, and Regression (after finetuning for each task separately): EuroBERT punches way above its weight."},{"type":"new_line","raw":"\n"},{"type":"text","value":"📝 Detailed paper with all details, incl. data: FineWeb for English and CulturaX for multilingual data, The Stack v2 and Proof-Pile-2 for code.","raw":"📝 Detailed paper with all details, incl. data: FineWeb for English and CulturaX for multilingual data, The Stack v2 and Proof-Pile-2 for code."},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Check out the release blogpost here: ","raw":"Check out the release blogpost here: "},{"type":"link","href":"https://huggingface.co/blog/EuroBERT/release","raw":"https://huggingface.co/blog/EuroBERT/release"},{"type":"new_line","raw":"\n"},{"type":"text","value":"* ","raw":"* "},{"type":"resource","resource":{"type":"model","id":"EuroBERT/EuroBERT-210m"},"url":"https://huggingface.co/EuroBERT/EuroBERT-210m","raw":"https://huggingface.co/EuroBERT/EuroBERT-210m"},{"type":"new_line","raw":"\n"},{"type":"text","value":"* ","raw":"* "},{"type":"resource","resource":{"type":"model","id":"EuroBERT/EuroBERT-610m"},"url":"https://huggingface.co/EuroBERT/EuroBERT-610m","raw":"https://huggingface.co/EuroBERT/EuroBERT-610m"},{"type":"new_line","raw":"\n"},{"type":"text","value":"* ","raw":"* "},{"type":"resource","resource":{"type":"model","id":"EuroBERT/EuroBERT-2.1B"},"url":"https://huggingface.co/EuroBERT/EuroBERT-2.1B","raw":"https://huggingface.co/EuroBERT/EuroBERT-2.1B"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"The next step is for researchers to build upon the 3 EuroBERT base models and publish strong retrieval, zero-shot classification, etc. models for all to use. I'm very much looking forward to it!","raw":"The next step is for researchers to build upon the 3 EuroBERT base models and publish strong retrieval, zero-shot classification, etc. models for all to use. I'm very much looking forward to it!"}],"rawContent":"An assembly of 18 European companies, labs, and universities have banded together to launch 🇪🇺 EuroBERT! It's a state-of-the-art multilingual encoder for 15 European languages, designed to be finetuned for retrieval, classification, etc. \n\n🇪🇺 15 Languages: English, French, German, Spanish, Chinese, Italian, Russian, Polish, Portuguese, Japanese, Vietnamese, Dutch, Arabic, Turkish, Hindi\n3️⃣ 3 model sizes: 210M, 610M, and 2.1B parameters - very very useful sizes in my opinion\n➡️ Sequence length of 8192 tokens! Nice to see these higher sequence lengths for encoders becoming more common.\n⚙️ Architecture based on Llama, but with bi-directional (non-causal) attention to turn it into an encoder. Flash Attention 2 is supported.\n🔥 A new Pareto frontier (stronger *and* smaller) for multilingual encoder models\n📊 Evaluated against mDeBERTa, mGTE, XLM-RoBERTa for Retrieval, Classification, and Regression (after finetuning for each task separately): EuroBERT punches way above its weight.\n📝 Detailed paper with all details, incl. data: FineWeb for English and CulturaX for multilingual data, The Stack v2 and Proof-Pile-2 for code.\n\nCheck out the release blogpost here: https://huggingface.co/blog/EuroBERT/release\n* https://huggingface.co/EuroBERT/EuroBERT-210m\n* https://huggingface.co/EuroBERT/EuroBERT-610m\n* https://huggingface.co/EuroBERT/EuroBERT-2.1B\n\nThe next step is for researchers to build upon the 3 EuroBERT base models and publish strong retrieval, zero-shot classification, etc. models for all to use. 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