We’re on a journey to advance and democratize artificial intelligence through open source and open science.

HuggingFace's platform is a resource for developers and researchers working in natural language processing (NLP) and machine learning, offering insights into NLP models, tools, and datasets. Through articles, tutorials, and open-source projects, HuggingFace offers insights into state-of-the-art NLP techniques, transformer architectures, and transfer learning methods. Developers can learn about using pre-trained models, fine-tuning strategies, and deploying NLP applications with HuggingFace's libraries and APIs.

Hugging Face

Hugging Face developed Jupyter Agent, a system that trains small language models to perform data science tasks by executing code in Jupyter notebooks. They created a comprehensive pipeline starting with 2TB of Kaggle notebooks, applied deduplication and quality filtering, generated synthetic question-answer pairs, and fine-tuned Qwen3-4B models. The approach achieved 75% accuracy on easy DABStep benchmark tasks, demonstrating that smaller models can become effective data science agents with proper training data and scaffolding. The project includes open-source datasets, trained models, and a simplified 200-line scaffolding system.

Jupyter Agents: training LLMs to reason with notebooks