RapidFire AI is an open-source Python engine to accelerate LLM fine-tuning and post-training with hyperparallel exeuction on the same resources and powerful interactive control to: stop, resume, clone-modify and warm-start configs on the fly. Branch experiments while they're running instead of starting from scratch or running one after another.
- Works within your OSS stack: PyTorch, HuggingFace TRL/PEFT), MLflow.
- Hyperparallel search: launch as many configs as you want together, even on a single GPU
- Dynamic real-time control: stop laggards, resume them later to revisit, branch promising configs in flight.
- Deterministic eval + run tracking: Metrics curves are automatically plotted and are comparable.
- Apache License v2.0: No vendor lock in. Develop on your IDE, launch from CLI.
📓 Try it now:
Try RapidFire AI in the Google Colab Notebook with no local installation—get started in under 3 minutes!
| Notebook | Description | Notebook Link |
|---|---|---|
| rf-colab-tensorboard-tutorial.ipynb | Interactive tutorial with TensorBoard integration |
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Links:
- GitHub rapidfireai
- 🐍 PyPI: https://pypi.org/project/rapidfireai/
- 📚 Docs: https://oss-docs.rapidfire.ai/
- 🌐 Company website: https://rapidfire.ai/
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We hope you enjoy the power of rapid experimentation with RapidFire AI for your LLM customization projects! We'd love to hear your feedback–both positive and negative–on the UX and UI, API, any rough edges, and what integrations and extensions you'd be excited to see.