37 lines
1.6 KiB
Markdown
37 lines
1.6 KiB
Markdown
# Finetuning on Intel GPU using Hugging Face PEFT code
|
|
|
|
This example demonstrates how to easily run LLM finetuning application of PEFT use IPEX-LLM 4bit optimizations using [Intel GPUs](../../../README.md). By applying IPEX-LLM patch, you could run Hugging Face PEFT code on Intel GPUs using IPEX-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 IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../README.md#requirements) for more information.
|
|
|
|
### 1. Install
|
|
|
|
```bash
|
|
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/
|
|
pip install transformers==4.34.0 datasets
|
|
pip install fire peft==0.5.0
|
|
pip install oneccl_bind_pt==2.1.100 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ # necessary to run distributed finetuning
|
|
pip install accelerate==0.23.0
|
|
pip install bitsandbytes scipy
|
|
```
|
|
|
|
### 2. Configures OneAPI environment variables
|
|
```bash
|
|
source /opt/intel/oneapi/setvars.sh
|
|
```
|
|
|
|
### 3. Finetune
|
|
|
|
This example shows how to run [Alpaca LoRA Training](https://github.com/tloen/alpaca-lora/tree/main) directly on Intel GPU.
|
|
|
|
```
|
|
cd alpaca-lora
|
|
python ./finetune.py --base_model "meta-llama/Llama-2-7b-hf" \
|
|
--data_path "yahma/alpaca-cleaned"
|
|
```
|