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@@ -13,15 +13,13 @@ This is the home for smol models (SmolLM) and high quality pre-training datasets
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  - [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu): a filtered version of FineWeb dataset for educational content, paper available [here](https://huggingface.co/papers/2406.17557).
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  - [Cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia): the largest open synthetic dataset, with 25B tokens and 30M samples. It contains synthetic textbooks, blog posts, and stories, posts generated by Mixtral. Blog post available [here](https://huggingface.co/blog/cosmopedia).
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  - [Smollm-Corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus): the pre-training corpus of SmolLM: **Cosmopedia v0.2**, **FineWeb-Edu dedup** and **Python-Edu**. Blog post available [here](https://huggingface.co/blog/smollm).
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- - [SmolLM models](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966) and [SmolLM2 models](https://huggingface.co/collections/HuggingFaceTB/smollm2-checkpoints-6723884218bcda64b34d7db9): a series of strong small models in three sizes: 135M, 360M and 1.7B
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  - [SmolVLM](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct): a 2 billion Vision Language Model (VLM) built for on-device inference. It uses SmolLM2-1.7B as a language backbone. Blog post available [here](https://huggingface.co/blog/smolvlm).
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- - [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath): a pretraining dataset with 50B tokens of mathematical and problem solving data.
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  **News πŸ—žοΈ**
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- - SmolLM2: you can find our most capable model SmolLM2-1.7B here: https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct and our training and evaluation toolkit at: https://github.com/huggingface/smollm
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- - We released our SFT mix SmolTalk, a 1M samples synthetic dataset to improve instruction following, chat and reasoning: https://hf.co/datasets/HuggingFaceTB/smoltalk
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- - SmolVLM: a lightweight 2B Vision Language Model available here https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct
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  <div align="center">
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  <img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/RvHjdlRT5gGQt5mJuhXH9.png" width="900"/>
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  </div>
 
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  - [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu): a filtered version of FineWeb dataset for educational content, paper available [here](https://huggingface.co/papers/2406.17557).
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  - [Cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia): the largest open synthetic dataset, with 25B tokens and 30M samples. It contains synthetic textbooks, blog posts, and stories, posts generated by Mixtral. Blog post available [here](https://huggingface.co/blog/cosmopedia).
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  - [Smollm-Corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus): the pre-training corpus of SmolLM: **Cosmopedia v0.2**, **FineWeb-Edu dedup** and **Python-Edu**. Blog post available [here](https://huggingface.co/blog/smollm).
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+ - [SmolLM2 models](https://huggingface.co/collections/HuggingFaceTB/smollm2-checkpoints-6723884218bcda64b34d7db9): a series of strong small models in three sizes: 135M, 360M and 1.7B
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  - [SmolVLM](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct): a 2 billion Vision Language Model (VLM) built for on-device inference. It uses SmolLM2-1.7B as a language backbone. Blog post available [here](https://huggingface.co/blog/smolvlm).
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+ - [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath): the best public math pretraining dataset with 50B tokens of mathematical and problem solving data.
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  **News πŸ—žοΈ**
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+ - **FineMath**: the best public math pretraining dataset with 50B tokens of mathematical and problem solving data https://huggingface.co/datasets/HuggingFaceTB/finemath
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+
 
 
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  <div align="center">
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  <img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/RvHjdlRT5gGQt5mJuhXH9.png" width="900"/>
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  </div>