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Loubna Ben Allal

loubnabnl

https://loubnabnl.github.io/

AI & ML interests

LLMs, ML for code, Synthetic data

Recent Activity

updated a dataset about 12 hours ago

HuggingFaceTB/smoltalk

updated a collection about 12 hours ago

SmolLM2

updated a collection about 12 hours ago

SmolLM2

Articles

SmolLM - blazingly fast and remarkably powerful

Jul 16

• 265

CodeGemma - an official Google release for code LLMs

Apr 9

• 99

Cosmopedia: how to create large-scale synthetic data for pre-training Large Language Models

Mar 20

• 66

Organizations

Posts 3

Post

4926

🍷 FineWeb technical report is out and so is 📚 FineWeb-Edu, a 1.3 trillion tokens dataset that outperforms all other open web datasets, with remarkable improvements on educational benchmarks such as MMLU, ARC, and OpenBookQA.

Technical report: HuggingFaceFW/blogpost-fineweb-v1
Dataset: HuggingFaceFW/fineweb-edu

We used Llama 3 generations to train an educational quality classifier, filtering the 15 trillion tokens of FineWeb to select only those with high educational value (an approach also used in Llama 3 and Phi-3 training datasets). We're releasing both FineWeb-Edu and the classifier, along with a larger, less heavily filtered version containing 5.4 trillion tokens.

You can find more details about the dataset and the experiments we ran in the FineWeb technical report, It's a 45-minute read but it contains all the secret sauce for building high quality web datasets.

Enjoy!

Post

6378

We've just published a detailed blog post on the creation of Cosmopedia dataset. We hope this will provide insights about generating synthetic data at scale for pre-training.
https://huggingface.co/blog/cosmopedia

Here are some key takeaways:
🎯 Prompt curation is crucial: we want to cover many topics with few duplicates.
📚 You can leverage various resources for diversity: using different seed data, generation formats, and target audiences.
⚙️ The importance of a good technical stack: for scalable generations with tools like llm-swarm and fast model training and evaluation.

Have a good read!

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