History

Wang, Jian4 9df70d95eb Refactor bigdl.llm to ipex_llm (#24 ) * Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm		2024-03-22 15:41:21 +08:00
..
alpaca-lora	Refactor bigdl.llm to ipex_llm (#24 )	2024-03-22 15:41:21 +08:00
README.md	LLM: add patching function for llm finetuning (#10247 )	2024-03-21 16:01:01 +08:00

README.md

Finetuning on Intel GPU using Hugging Face PEFT code

This example demonstrates how to easily run LLM finetuning application of PEFT use BigDL-LLM 4bit optimizations using Intel GPUs. By applying BigDL-LLM patch, you could run Hugging Face PEFT code on Intel GPUs using BigDL-LLM optimization without modification.

Note, this example is just used for illustrating related usage and don't guarantee convergence of training.

0. Requirements

To run this example with BigDL-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=3.9
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
pip install transformers==4.34.0 datasets
pip install fire peft==0.5.0
pip install oneccl_bind_pt==2.1.100 -f https://developer.intel.com/ipex-whl-stable-xpu # necessary to run distributed finetuning
pip install accelerate==0.23.0
pip install bitsandbytes scipy

2. Configures OneAPI environment variables

source /opt/intel/oneapi/setvars.sh

3. Finetune

This example shows how to run Alpaca LoRA Training directly on Intel GPU.

cd alpaca-lora
python ./finetune.py --base_model "meta-llama/Llama-2-7b-hf" \
                     --data_path "yahma/alpaca-cleaned"