FATE-LLM
FATE-LLM is a framework to support federated learning for large language models(LLMs) and small language models(SLMs).
Design Principle
- Federated learning for large language models(LLMs) and small language models(SLMs).
- 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.
Standalone deployment
- To deploy FATE-LLM v2.2.0 or higher version, three ways are provided, please refer deploy tutorial for more details:
- deploy with FATE only from pypi then using Launcher to run tasks
- deploy with FATE、FATE-Flow、FATE-Client from pypi, user can run tasks with Pipeline
- To deploy lower versions: please refer to FATE-Standalone deployment.
- To deploy FATE-LLM v2.0. - FATE-LLM v2.1., deploy FATE-Standalone with version >= 2.1, then make a new directory
{fate_install}/fate_llm
and clone the code into it, install the python requirements, and add {fate_install}/fate_llm/python
to PYTHONPATH
- To deploy FATE-LLM v1.x, deploy FATE-Standalone 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
FATE-LLM Evaluate
Citation
If you publish work that uses FATE-LLM, please cite FATE-LLM as follows:
@article{fan2023fate,
title={Fate-llm: A industrial grade federated learning framework for large language models},
author={Fan, Tao and Kang, Yan and Ma, Guoqiang and Chen, Weijing and Wei, Wenbin and Fan, Lixin and Yang, Qiang},
journal={Symposium on Advances and Open Problems in Large Language Models (LLM@IJCAI'23)},
year={2023}
}