ipex-llm/python/llm/example/GPU/LLM-Finetuning/QLoRA/trl-example/README.md

Example of QLoRA Finetuning with IPEX-LLM

This simple example demonstrates how to finetune a llama2-7b model use IPEX-LLM 4bit optimizations with TRL library on Intel GPU. Note, this example is just used for illustrating related usage and don't guarantee convergence of training.

0. Requirements

To run this example with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to here for more information.

Example: Finetune llama2-7b using qlora

This example utilizes a subset of yahma/alpaca-cleaned for training. And the export_merged_model.py is ported from alpaca-lora.

1. Install

conda create -n llm python=3.9
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
pip install transformers==4.34.0 datasets
pip install peft==0.5.0
pip install accelerate==0.23.0
pip install bitsandbytes scipy trl

2. Configures OneAPI environment variables

source /opt/intel/oneapi/setvars.sh

3. Finetune model

python ./qlora_finetuning.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH

Sample Output

{'loss': 3.1898, 'learning_rate': 2e-05, 'epoch': 0.02}
{'loss': 3.1854, 'learning_rate': 1.7777777777777777e-05, 'epoch': 0.03}
{'loss': 3.0359, 'learning_rate': 1.555555555555556e-05, 'epoch': 0.05}
{'loss': 2.9661, 'learning_rate': 1.3333333333333333e-05, 'epoch': 0.06}
{'loss': 2.7779, 'learning_rate': 1.1111111111111113e-05, 'epoch': 0.08}                              
{'loss': 2.7795, 'learning_rate': 8.888888888888888e-06, 'epoch': 0.09}
{'loss': 2.5149, 'learning_rate': 6.666666666666667e-06, 'epoch': 0.11}
{'loss': 2.5759, 'learning_rate': 4.444444444444444e-06, 'epoch': 0.12}
{'loss': 2.5976, 'learning_rate': 2.222222222222222e-06, 'epoch': 0.14}
{'loss': 2.5744, 'learning_rate': 0.0, 'epoch': 0.15}
{'train_runtime': 116.1914, 'train_samples_per_second': 6.885, 'train_steps_per_second': 1.721, 'train_loss': 2.819730052947998, 'epoch': 0.15}                                                          
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [01:56<00:00,  1.72it/s]
TrainOutput(global_step=200, training_loss=2.819730052947998, metrics={'train_runtime': 116.1914, 'train_samples_per_second': 6.885, 'train_steps_per_second': 1.721, 'train_loss': 2.819730052947998, 'epoch': 0.15})

4. Merge the adapter into the original model

python ./export_merged_model.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --adapter_path ./outputs/checkpoint-200 --output_path ./outputs/checkpoint-200-merged

Then you can use ./outputs/checkpoint-200-merged as a normal huggingface transformer model to do inference.