de4bb97b4f
*ipex-llm's accelerate has been upgraded to 0.23.0. Remove accelerate 0.23.0 install command in README and docker。
36 lines
1.5 KiB
Markdown
36 lines
1.5 KiB
Markdown
# Finetuning on Intel GPU using Hugging Face PEFT code
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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.
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Note, this example is just used for illustrating related usage and don't guarantee convergence of training.
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### 0. Requirements
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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.
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### 1. Install
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```bash
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conda create -n llm python=3.11
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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pip install transformers==4.36.0 datasets
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pip install fire peft==0.10.0
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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
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pip install bitsandbytes scipy
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```
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### 2. Configures OneAPI environment variables
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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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### 3. Finetune
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This example shows how to run [Alpaca LoRA Training](https://github.com/tloen/alpaca-lora/tree/main) directly on Intel GPU.
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```
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cd alpaca-lora
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python ./finetune.py --base_model "meta-llama/Llama-2-7b-hf" \
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--data_path "yahma/alpaca-cleaned"
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```
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