mLoRA (a.k.a Multi-LoRA Fine-Tune) is an open-source framework designed for efficient fine-tuning of multiple Large Language Models (LLMs) using LoRA and its variants. Key features of mLoRA include:
-
Concurrent fine-tuning of multiple LoRA adapters.
-
Shared base model among multiple LoRA adapters.
-
Efficient pipeline parallelism algorithm.
-
Support for multiple LoRA variant algorithms and various base models.
-
Support for multiple reinforcement learning preference alignment algorithms.
Firstly, you should clone this repository and install dependencies:
# Clone Repository
git clone https://github.com/TUDB-Labs/mLoRA
cd mLoRA
# Install requirements need the Python >= 3.12
pip install .
The mlora.py
code is a starting point for batch fine-tuning LoRA adapters.
python mlora.py \
--base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \
--config demo/lora/lora_case_1.yaml
You can check the adapters' configuration in demo folder, there are some configuration regarding the use of different LoRA variants and reinforcement learning preference alignment algorithms.
For further detailed usage information, please use --help
option:
python mlora.py --help
We can deploy mLoAR as a service to continuously receive user requests and perform fine-tuning task.
# Install requirements for deploy
pip install .[deploy]
# Start the server
python mlora_server.py \
--base_model /data/TinyLlama-1.1B-Chat-v1.0/ \
--root /tmp/mlora
For further detailed usage information, please use --help
option:
python mlora_server.py --help
Once the service is deployed, use mlora_cli.py
to interact with the server.
python mlora_cli.py
Using mLoRA can save significant computational and memory resources when training multiple adapters simultaneously.
We fine-tuned multiple LoRA adapters using four A6000 graphics cards with fp32 precision and without using checkpointing and any quantization techniques:
Model | mLoRA (tokens/s) | PEFT-LoRA with FSDP (tokens/s) | PEFT-LoRA with TP (tokens/s) |
---|---|---|---|
llama-2-7b (32fp) | 2364 | 1750 | 1500 |
llama-2-13b (32fp) | 1280 | OOM | 875 |
Model | |
---|---|
✓ | LLaMA |
Variant | |
---|---|
✓ | QLoRA |
✓ | LoRA+ |
Variant | |
---|---|
✓ | DPO |
✓ | CPO |
- Help Document[TODO]
- Design Document
We welcome contributions to improve this repository! Please review the contribution guidelines before submitting pull requests or issues.
Fork the repository. Create a new branch for your feature or fix. Submit a pull request with a detailed explanation of your changes.
You can use the pre-commit to check your code.
# Install requirements
pip install .[ci_test]
ln -s ../../.github/workflows/pre-commit .git/hooks/pre-commit
Or just call the script to check your code
.github/workflows/pre-commit
Please cite the repo if you use the code in this repo.
@misc{m-LoRA,
author = {Zhengmao, Ye\textsuperscript{*} and Dengchun, Li\textsuperscript{*} and Jingqi, Tian and Tingfeng, Lan and Yanbo, Liang and Yexi, Jiang and Jie, Zuo and Hui, Lu and Lei, Duan and Mingjie, Tang},
title = {m-LoRA: Efficient LLM Model Fine-tune and Inference via Multi-Lora Optimization},
year = {2023},
publisher = {GitHub},
howpublished = {\url{https://github.com/TUDB-Labs/mLoRA}},
note={\textsuperscript{*}: these authors contributed equally to this work.}
}
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