README.md

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title: README
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# HuggingFaceTB
This is the home for smol models (SmolLM) and high quality pre-training datasets. We released:

- [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).
- [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).
- [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).
- [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
- [SmolVLM](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct): a 2 billion Vision Lnaguage 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).

**News 🗞️**
- 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
- We released our SFT mix SmolTalk, a 1M samples synthetic dataset to improve instruction following, chat and reasoning: https://hf.co/datasets/HuggingFaceTB/smoltalk
- SmolVLM: a lightweight 2B Vision Language Model available here https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct

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