https://github.com/adrianbzg/llm-distributed-finetune

Tune efficiently any LLM model from HuggingFace using distributed training (multiple GPU) and DeepSpeed. Uses Ray AIR to orchestrate the training on multiple AWS GPU instances
https://github.com/adrianbzg/llm-distributed-finetune

aws deep-learning distributed-training falcon fine-tuning huggingface large-language-models natural-language-processing transformers

Last synced: 8 days ago
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Tune efficiently any LLM model from HuggingFace using distributed training (multiple GPU) and DeepSpeed. Uses Ray AIR to orchestrate the training on multiple AWS GPU instances

• Host: GitHub
• URL: https://github.com/adrianbzg/llm-distributed-finetune
• Owner: AdrianBZG
• License: mit
• Created: 2023-06-18T10:54:23.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-06-20T08:25:26.000Z (over 1 year ago)
• Last Synced: 2024-05-22T13:31:06.129Z (6 months ago)
• Topics: aws, deep-learning, distributed-training, falcon, fine-tuning, huggingface, large-language-models, natural-language-processing, transformers
• Language: Python
• Homepage:
• Size: 20.9 MB
• Stars: 46
• Watchers: 4
• Forks: 6
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Finetuning Large Language Models Efficiently on a Distributed Cluster

This repository is a boilerplate/fingerprint to fine tune any HuggingFace Large Language Model, such as FALCON-7B, using a distributed cluster.

The purpose of this repo is to make it straightforward to fine tune any model efficiently by leveraging multi-GPU training.

It uses Ray AIR to orchestrate the cluster on AWS, and DeepSpeed for parameter+optimizer sharding + offloading.

The following FALCON-7B model was fine-tuned using this repo: [https://huggingface.co/AdrianBZG/falcon-7b-spanish-8bit](https://huggingface.co/AdrianBZG/falcon-7b-spanish-8bit)

## Setup

First, you need to clone the repo:

`git clone https://github.com/AdrianBZG/LLM-distributed-finetune`

Then, configure your aws credentials using the `awscli` package command `aws configure`. This will allow Ray to spawn the head node and provision workers with the auto-scaling mechanism. If you don't have `awscli`, you can install it using `pip install awscli`.

## Working with the Ray cluster and submitting finetuning jobs

To spawn the cluster, simply run:

`ray up ray_cluster.yaml`

Once Ray has finished setting up the cluster, you can attach to the head node by doing:

`ray attach ray_cluster.yaml`

Now, to run a finetuning job, you can use the script `finetune.py` under `/src`.

An example usage is as below:

`python finetune.py --model="tiiuae/falcon-7b" --num-workers 4 --data alpaca_data_cleaned.json`

This will run a finetuning on the FALCON-7B model using 4 GPU workers, and the Alpaca instruction dataset. Feel free to adjust the arguments for your own purposes.

When you are finished, you can turn off the cluster with:

`ray down ray_cluster.yaml`

## Changing DeepSpeed configuration

To tune the DeepSpeed configuration for your specific use case, edit the file on `config/deepspeed.json`. If you want to disable DeepSpeed, you can pass the `--no-deepspeed` parameter to the `finetune.py` script.

# Datasets

I have successfully fine-tuned FALCON-7B on the following 2 datasets:

- Alpaca: [https://huggingface.co/datasets/yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned)

- Alpaca Spanish: [https://huggingface.co/datasets/bertin-project/alpaca-spanish](https://huggingface.co/datasets/bertin-project/alpaca-spanish)