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

Finetune LLM on Intel GPU using axolotl v0.3.0 without writing code

This example demonstrates how to easily run LLM finetuning application using axolotl v0.3.0 and IPEX-LLM 4bit optimizations with Intel GPUs. By applying IPEX-LLM patch, you could use axolotl on Intel GPUs using IPEX-LLM optimization without writing code.

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.

1. Install

conda create -n llm python=3.11
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/
# install axolotl v0.3.0
git clone https://github.com/OpenAccess-AI-Collective/axolotl
cd axolotl
git checkout v0.3.0
cp ../requirements-xpu.txt requirements.txt
pip install -e .

2. Configures OneAPI environment variables and accelerate

Configures OneAPI environment variables

source /opt/intel/oneapi/setvars.sh

Configures accelerate in command line interactively.

accelerate config

Please answer NO in option Do you want to run your training on CPU only (even if a GPU / Apple Silicon device is available)? [yes/NO]:.

After finish accelerate config, check if use_cpu is disable (i.e., use_cpu: false) in accelerate config file (~/.cache/huggingface/accelerate/default_config.yaml).

3. Finetune Llama-2-7B

This example shows how to run Alpaca QLoRA finetune on Llama-2 directly on Intel GPU, based on axolotl Llama-2 qlora example. Note that only Llama-2-7B QLora example is verified on Intel ARC 770 with 16GB memory.

Modify parameters in qlora.yml based on your requirements.

accelerate launch finetune.py qlora.yml

Output in console

{'eval_loss': 0.9382301568984985, 'eval_runtime': 6.2513, 'eval_samples_per_second': 3.199, 'eval_steps_per_second': 3.199, 'epoch': 0.36}
{'loss': 0.944, 'learning_rate': 0.00019752490425051743, 'epoch': 0.38}
{'loss': 1.0179, 'learning_rate': 0.00019705675197106016, 'epoch': 0.4}
{'loss': 0.9346, 'learning_rate': 0.00019654872959986937, 'epoch': 0.41}
{'loss': 0.9747, 'learning_rate': 0.0001960010458282326, 'epoch': 0.43}
{'loss': 0.8928, 'learning_rate': 0.00019541392564000488, 'epoch': 0.45}
{'loss': 0.9317, 'learning_rate': 0.00019478761021918728, 'epoch': 0.47}
{'loss': 1.0534, 'learning_rate': 0.00019412235685085035, 'epoch': 0.49}
{'loss': 0.8777, 'learning_rate': 0.00019341843881544372, 'epoch': 0.5}
{'loss': 0.9447, 'learning_rate': 0.00019267614527653488, 'epoch': 0.52}
{'loss': 0.9651, 'learning_rate': 0.00019189578116202307, 'epoch': 0.54}
{'loss': 0.9067, 'learning_rate': 0.00019107766703887764, 'epoch': 0.56}