MCP ServersLiu
Created at 3 months ago
by modelscope
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trinity-rft
reinforcement-learning
language-models
What is Trinity-RFT?
Trinity-RFT is a general-purpose, flexible framework designed for reinforcement fine-tuning (RFT) of large language models (LLMs). It aims to support diverse application scenarios and serves as a unified platform for exploring advanced reinforcement learning paradigms.
How to use Trinity-RFT?
To use Trinity-RFT, clone the repository from GitHub, set up your environment, and follow the installation instructions. You can configure the RFT process through a web interface or command line, and run the training process using provided examples.
Key features of Trinity-RFT?
- Unified RFT core supporting various training modes (synchronous/asynchronous, on-policy/off-policy).
- First-class agent-environment interaction handling lagged feedback and multi-turn interactions.
- Optimized data pipelines for active management of rollout tasks.
- User-friendly design with modular architecture and graphical interfaces for low-code usage.
Use cases of Trinity-RFT?
- Adapting to new agent-environment scenarios.
- Developing custom reinforcement learning algorithms.
- Monitoring and tracking the learning process through graphical interfaces.
FAQ from Trinity-RFT?
- Is Trinity-RFT suitable for all types of reinforcement learning tasks?
Yes, Trinity-RFT is designed to be flexible and can be adapted to various RL tasks and scenarios.
- What are the system requirements for Trinity-RFT?
Trinity-RFT requires Python 3.10-3.12 and at least 2 GPUs for optimal performance.
- How can I contribute to Trinity-RFT?
Contributions are welcome! You can follow the contribution guide in the repository.
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