The Open R1 project offers a suite of powerful features, primarily including:
- model trainingProvides scripts for training models, supporting both GRPO and SFT training methods.
- Model EvaluationProvides scripts for evaluating model performance, supporting R1 benchmarking.
- Data generationA script can be used to generate synthetic data with Distilabel.
- Multi-stage trainingDemonstrates a multi-stage complete training process from foundational models to reinforcement learning optimization.
- Community collaborationSupport community members in contributing datasets and model improvements.
The combination of these features makes Open R1 a complete DeepSeek-R1 replication platform, capable not only of reproducing the original training process but also enabling innovation and improvements upon it.
Particularly noteworthy is that the project's multi-stage training functionality effectively replicates the original DeepSeek-R1 training workflow. This includes reproducing the R1-Distill model, constructing a pure RL pipeline, and executing the final model tuning process—all of which greatly aid in understanding and utilizing DeepSeek-R1 technology.
This answer comes from the articleOpen R1: Hugging Face Replicates the Training Process of DeepSeek-R1The
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