pinglmlcv / FATE-LLM

Federated Learning for LLMs.

FATE-LLM

FATE-LLM is a framework to support federated learning for large language models(LLMs).

Design Principle

• Federated learning for heterogeneous large model and small model.
• Promote training efficiency of federated LLMs using Parameter-Efficient methods.
• Protect the IP of LLMs using FedIPR.
• Protect data privacy during training and inference through privacy preserving mechanisms.

Deployment

Standalone deployment

Please refer to FATE-Standalone deployment.
Deploy FATE-Standalone version with 1.11.3 <= version < 2.0, then copy directory python/fate_llm to {fate_install}/fate/python/fate_llm

Cluster deployment

Use FATE-LLM deployment packages to deploy, refer to FATE-Cluster deployment for more deployment details.

Quick Start

• Offsite-tuning Tutorial: Model Definition and Job Submission
• FedIPR Tutorial: Add Watermarks to Your Model
• Federated ChatGLM-6B Training
• GPT-2 Training
• Builtin Models In PELLM