Hugging Face releases ML-Intern, its open-source agent for the model-training loop
The project turns Hugging Face's papers, datasets, Spaces and Jobs stack into an autonomous workflow for experiments and post-training.
By Ryan Merket · Published
Primary source: Aligned News - AI Intelligence
Why it matters
ML-Intern turns Hugging Face's Hub from a place to host ML assets into a place where agents can research, train, evaluate and spend compute.
Hugging Faces Lewis Tunstall and Aksel Joonas Reedi moved ML-Intern out of beta on June 18th, releasing an open-source agent that tries to automate the machine learning research loop from literature review to training runs.
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