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Liu MCP Server

@modelscope

リポジトリ

基本情報

カテゴリ

その他

トランスポート

stdio

作者

modelscope

投稿者

liu

Liu MCP Server について

概要はまだありません

#trinity-rft#reinforcement-learning#language-models

Liu MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

Liu MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Liu?

Liu is a general‑purpose, flexible and easy‑to‑use framework for reinforcement fine‑tuning (RFT) of large language models. It supports diverse application scenarios and serves as a unified platform for exploring advanced RL paradigms, with capabilities for synchronous/asynchronous, on‑policy/off‑policy, and online/offline training.

How to use Liu?

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.

Key features of Liu

  • 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.

Use cases of Liu

  • Adapting LLMs to new multi‑turn or multi‑step agentic scenarios.
  • Developing custom RL algorithms with plug‑and‑play classes.
  • Low‑code monitoring and tracking of the learning process.
  • Fine‑tuning models on reasoning tasks (e.g., GSM8k with GRPO).
  • Running offline learning via DPO or SFT.

よくある質問

1

What are the system requirements for Liu?

Python version ≥3.10 and ≤3.12, CUDA ≥12.4 and ≤12.8, and at least 2 GPUs are required.

2

Which models and datasets are supported?

Liu supports most datasets and models from Huggingface and ModelScope, downloaded via `huggingface-cli` or `modelscope`.

3

How do I run a typical RFT process?

After installing and starting a Ray cluster, run `trinity run --config <config_path>` or use the web interface’s “Run” button after configuration.

4

Does Liu support multi‑turn and agentic interactions?

Yes, it supports first‑class agent‑environment interaction with lagged feedback, long‑tailed latencies, and graceful failure handling for multi‑turn tasks.

5

Can I use Liu for offline learning?

Yes, Liu supports offline learning modes, for example through DPO or SFT, as described in its tutorials.

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