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🤖🔧 LLM Fine Tuning

This repository contains the Union.ai open source codebase for running LLM fine-tuning jobs on Flyte or Union Cloud.

💻 Setup

python -m venv ~/venvs/llm-fine-tuning
source ~/venvs/llm-fine-tuning/bin/activate
pip install -r requirements.txt

Export Environment Variables

export PYTHONPATH=$(pwd):$PYTHONPATH
export FLYTECTL_CONFIG=~/.uctl/config-demo.yaml  # replace with your flyte/union cloud config
export REGISTRY=ghcr.io/unionai-oss  # replace this with your own registry
export FLYTE_PROJECT=llm-fine-tuning
export IMAGE=ghcr.io/unionai-oss/unionai-llm-fine-tuning:de445a0
export IMAGE=ghcr.io/unionai-oss/unionai-llm-fine-tuning:c7df70e

🐳 Container Build [Optional]

This repository comes with a pre-built image for running the fine-tuning workflows, but if you want to build your own, follow these instructions to build an image with transformers and deepspeed pre-built.

docker login ghcr.io
gitsha=$(git rev-parse --short=7 HEAD)
image_name=$REGISTRY/unionai-llm-fine-tuning
docker build . -t $image_name:$gitsha -f Dockerfile
docker push $image_name:$gitsha

🚀 Run on a Flyte or Union Cloud Cluster

The instructions below will run on your Flyte or Union Cloud cluster assuming you have the follow prerequisites:

Create Project

First, install flytectl.

Then, create a project with:

flytectl --config $FLYTECTL_CONFIG create project  \
  --id "llm-fine-tuning" \
  --description "Fine-tuning for LLMs" \
  --name "llm-fine-tuning"

🔀 Fine-tuning Workflows

The fine_tuning directory contains Flyte tasks and workflows for fine-tuning LLMs:

  • fine_tuning/llm_fine_tuning.py: Full fine-tuning using DeepSpeed
  • fine_tuning/llm_tuning_lora.py: Parameter-efficient fine-tuning using LoRA
  • fine_tuning/llm_tuning_qlora.py: Parameter-efficient fine-tuning using 4-bit QLoRA

⚙️ Configuration

The config directory contains *.json files which correspond to different configurations for fine-tuning. These are used to determine the model, dataset, training arguments, and model publishing arguments.

👟 Execute Fine-tuning Workflows on the CLI

Local Fine-tuning (facebook/opt-125m) 

pyflyte run \
    fine_tuning/llm_fine_tuning.py fine_tune \
    --config config/training_config_local.json \
    --deepspeed_config "{}"

RedPajama Fine-tuning

Full Fine-tuning (togethercomputer/RedPajama-INCITE-Base-3B-v1) 

pyflyte --config $FLYTECTL_CONFIG \
    run --remote \
    --copy-all \
    --project $FLYTE_PROJECT \
    --image $IMAGE \
    fine_tuning/llm_fine_tuning.py fine_tune \
    --config config/training_config_redpajama_3b.json \
    --deepspeed_config config/deepspeed.json

LoRA Fine-tuning (togethercomputer/RedPajama-INCITE-7B-Base) 

pyflyte --config $FLYTECTL_CONFIG \
    run --remote \
    --copy-all \
    --project $FLYTE_PROJECT \
    --image $IMAGE \
    fine_tuning/llm_fine_tuning_lora.py fine_tune \
    --config config/training_config_redpajama_7b_lora.json

Llama2 Fine-tuning

Full Fine-tuning (meta-llama/Llama-2-7b-hf) 

pyflyte --config $FLYTECTL_CONFIG \
    run --remote \
    --copy-all \
    --project $FLYTE_PROJECT \
    --image $IMAGE \
    fine_tuning/llm_fine_tuning.py fine_tune \
    --config config/training_config_llama2_7b.json \
    --deepspeed_config config/deepspeed.json

Full Fine-tuning (meta-llama/Llama-2-13b-hf) 

pyflyte --config $FLYTECTL_CONFIG \
    run --remote \
    --copy-all \
    --project $FLYTE_PROJECT \
    --image $IMAGE \
    fine_tuning/llm_fine_tuning.py fine_tune \
    --config config/training_config_llama2_13b.json \
    --deepspeed_config config/deepspeed_llama2_13b.json

QLoRA Fine-tuning (meta-llama/Llama-2-13b-hf) 

pyflyte --config $FLYTECTL_CONFIG \
    run --remote \
    --copy-all \
    --project $FLYTE_PROJECT \
    --image $IMAGE \
    fine_tuning/llm_fine_tuning_qlora.py fine_tune \
    --config config/training_config_llama2_13b_qlora.json

QLoRA Fine-tuning (meta-llama/Llama-2-70b-hf) 

pyflyte --config $FLYTECTL_CONFIG \
    run --remote \
    --copy-all \
    --project $FLYTE_PROJECT \
    --image $IMAGE \
    fine_tuning/llm_fine_tuning_qlora.py fine_tune \
    --config config/training_config_llama2_70b_qlora.json