ML-intern: an open-source ML engineer that reads papers, trains and ships models
ML Intern is an open-source tool designed for machine learning engineers to autonomously research, write, and deploy code using the Hugging Face ecosystem. It supports various models and allows users to interact with them in both interactive and headless modes. The tool also provides options for local model support and automatic session uploads to a private Hugging Face dataset.
- ▪ML Intern can autonomously research and write ML-related code using the Hugging Face ecosystem.
- ▪Users can interact with the tool in both interactive and headless modes for different use cases.
- ▪The tool supports local model integration and allows for automatic uploads of session data to a private dataset.
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ML Intern An ML intern that autonomously researches, writes, and ships good quality ML related code using the Hugging Face ecosystem — with deep access to docs, papers, datasets, and cloud compute. Quick Start Installation git clone [email protected]:huggingface/ml-intern.git cd ml-intern uv sync uv tool install -e . That's it. Now ml-intern works from any directory: ml-intern Create a .env file in the project root (or export these in your shell): ANTHROPIC_API_KEY=<your-anthropic-api-key> # if using anthropic models OPENAI_API_KEY=<your-openai-api-key> # if using openai models LOCAL_LLM_BASE_URL=http://localhost:8000 # shared fallback for local model prefixes LOCAL_LLM_API_KEY=<optional-local-api-key> # optional shared local API key HF_TOKEN=<your-hugging-face-token>…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.