Liu

by modelscope

260 downloads
Not rated
GitHub

Description

[**中文主页**](https://github.com/modelscope/Trinity-RFT/blob/main/README_zh.md) | [**Tutorial**](https://modelscope.github.io/Trinity-RFT/) | [**FAQ**](./docs/sphinx_doc/source/tutorial/faq.md) <div align="center"> <img…

About

[**中文主页**](https://github.com/modelscope/Trinity-RFT/blob/main/README_zh.md) | [**Tutorial**](https://modelscope.github.io/Trinity-RFT/) | [**FAQ**](./docs/sphinx_doc/source/tutorial/faq.md) <div align="center"> <img src="https://img.alicdn.com/imgextra/i1/O1CN01lvLpfw25Pl4ohGZnU_!!6000000007519-2-tps-1628-490.png"…

Details

Author
modelscope
Downloads
260
Categories
Other

- Unified RFT core supporting multiple training modes.
- First‑class agent‑environment interaction handling.
- Optimized data pipelines with active management.
- Modular, decoupled architecture for easy adoption.
- Web‑based graphical interfaces for low‑code usage.

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name Liu
    Command (node, npx, python, etc.)

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. Enable "Start Automatically" if you want the plugin to start when Highlight launches

From the repository

Install Liu via pip install trinity-rft==0.2.1 or from source (Python ≥3.10, CUDA ≥12.4, at least 2 GPUs required). Prepare a model and dataset from Huggingface or ModelScope, then launch a web interface with trinity studio --port 8080 to configure and run the RFT process, or use trinity run --config <config_path> on the command line after starting a Ray cluster.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "liu": {
            "Trinity-RFT": {
                "command": "docker",
                "args": [
                    "build",
                    "-f",
                    "scripts/docker/Dockerfile",
                    "-t",
                    "trinity-rft:latest",
                    "."
                ]
            }
        }
    }
}

McpServers

{
    "Trinity-RFT": {
        "command": "docker",
        "args": [
            "build",
            "-f",
            "scripts/docker/Dockerfile",
            "-t",
            "trinity-rft:latest",
            "."
        ]
    }
}

💡 What is Trinity-RFT?

Trinity-RFT is a general-purpose, flexible and easy-to-use framework for reinforcement fine-tuning (RFT) of large language models (LLM).
It is designed to support diverse application scenarios and serve as a unified platform for exploring advanced RL paradigms in the era of experience.

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