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README.md

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 # HuggingFaceTB
 This is the home for small LLMs (SmolLM) and high quality pre-training datasets, such as [Cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) and [Smollm-Corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus). 
 
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 We released:
 
-- [Cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia): the largest open synthetic dataset, with 25B tokens and more than 30M samples. It contains synthetic textbooks, blog posts, stories, posts, and WikiHow articles generated by Mixtral-8x7B-Instruct-v0.1.
-- [Cosmo-1B](https://huggingface.co/HuggingFaceTB/cosmo-1b) a 1B model trained on Cosmopedia.
-- [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu): a filtered version of FineWeb dataset for educational content
-- [Smollm-Corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus): the pre-training corpus of SmolLM models including **Cosmopedia v0.2**, **FineWeb-Edu** and **Python-Edu**.
-- [SmolLM models](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966) and [SmolLM2](https://huggingface.co/collections/HuggingFaceTB/smollm2-checkpoints-6723884218bcda64b34d7db9): a series of strong small models in three sizes: 135M, 360M and 1.7B
+- [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 more than 30M samples. It contains synthetic textbooks, blog posts, stories, posts, and WikiHow articles generated by Mixtral-8x7B-Instruct-v0.1. Blog post available [here](https://huggingface.co/blog/cosmopedia).
+- [Smollm-Corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus): the pre-training corpus of SmolLM models including **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
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+**News 🗞️**
+- SmolLM2: you can find our most capable model SmolLM2-1.7B here: https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct
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+<img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/kIUMhq5p_7Vl74lihlS8y.png" width="600"/>
+<p><em>Evaluation of SmolLM2 and other models on common benchmarks. For more details, refer to the <a href="https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct/" target="_blank">model card</a>.</em></p>
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+- We released our SFT mix SmolTalk, a diverse dataset of 1M synthetic instruction and answer pairs to improve instruction following and reasoning: https://huggingface.co/datasets/HuggingFaceTB/smoltalk
 
-For more details check our blog posts: https://huggingface.co/blog/cosmopedia and https://huggingface.co/blog/smollm
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+<img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/hdA2qVsKtoZnPKltNbvZB.png" width="800"/>
+<p><em>Comparison of models finetuned on SmolTalk and Orca AgentInstruct 1M. For more details, refer to the <a href="https://huggingface.co/datasets/HuggingFaceTB/smoltalk" target="_blank">dataset card</a>.</em></p>
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