Hugging Face’s Trio of Innovation: Transforming LLM Training and Evaluation with nanotron, DataTrove and LightEval

Just a few days ago, Hugging Face open-sourced DataTrove, nanotron, and LightEval — three cutting-edge libraries that will help you process massive datasets, and train/evaluate LLMs across expansive GPU landscapes.

And the great news is that you can achieve all within a remarkably concise codebase of less than a few thousands lines of code!

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Whether you’re operating on a single GPU or dreaming of a GPU farm, you should definitely get familiar with these libraries. It will help you a ton to develop an intuition about text data preparation and large model training and evaluation.

In this article, I will walk you through:

• Setting up the local environment
• Usage and code examples for DataTrove, nanotron, and LightEval
• Further resources to dig deeper

Let’s go!

Setting up the environment

Let’s start by setting up our virtual environment:

# Create a virtual environment
mkdir hf-data-nano-light && cd hf-data-nano-light
python3 -m…