104 lines
4.1 KiB
Markdown
104 lines
4.1 KiB
Markdown
# Finetune LLM on Intel GPU using axolotl v0.4.0 without writing code
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This example demonstrates how to easily run LLM finetuning application using [axolotl v0.4.0](https://github.com/OpenAccess-AI-Collective/axolotl/tree/v0.4.0) and IPEX-LLM 4bit optimizations with [Intel GPUs](../../../README.md). By applying IPEX-LLM patch, you could use axolotl on Intel GPUs using IPEX-LLM optimization without writing code.
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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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# install axolotl v0.4.0
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git clone https://github.com/OpenAccess-AI-Collective/axolotl
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cd axolotl
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git checkout v0.4.0
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cp ../requirements-xpu.txt requirements.txt
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pip install -e .
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pip install transformers==4.36.0
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```
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### 2. Configures OneAPI environment variables and accelerate
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#### 2.1 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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#### 2.2 Configures `accelerate` in command line interactively.
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```bash
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accelerate config
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```
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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]:`.
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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`).
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#### 2.3 (Optional) Set ` HF_HUB_OFFLINE=1` to avoid huggingface hug signing.
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```bash
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export HF_HUB_OFFLINE=1
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```
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For more details, please refer [hfhuboffline](https://huggingface.co/docs/huggingface_hub/en/package_reference/environment_variables#hfhuboffline).
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### 3. Finetune Llama-2-7B
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This example shows how to run [Alpaca LoRA training](https://github.com/tloen/alpaca-lora/tree/main) and [Alpaca QLoRA finetune](https://github.com/artidoro/qlora) directly on Intel GPU. Note that only Llama-2-7B LoRA and QLoRA examples are verified on Intel ARC 770 with 16GB memory.
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#### 3.1 Alpaca LoRA
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Based on [axolotl Llama-2 LoRA example](https://github.com/OpenAccess-AI-Collective/axolotl/blob/v0.4.0/examples/llama-2/lora.yml).
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```
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accelerate launch finetune.py lora.yml
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```
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In v0.4.0, you can also use `train.py` instead of `-m axolotl.cli.train` or `finetune.py`.
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```
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accelerate launch train.py lora.yml
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```
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#### 3.2 Alpaca QLoRA
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Based on [axolotl Llama-2 QLoRA example](https://github.com/OpenAccess-AI-Collective/axolotl/blob/v0.4.0/examples/llama-2/qlora.yml).
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Modify parameters in `qlora.yml` based on your requirements. Then, launch finetuning with the following command.
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```
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accelerate launch finetune.py qlora.yml
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```
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In v0.4.0, you can also use `train.py` instead of `-m axolotl.cli.train` or `finetune.py`.
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```
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accelerate launch train.py qlora.yml
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```
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#### 3.3 Expected Output
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Output in console
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```
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{'eval_loss': 0.9382301568984985, 'eval_runtime': 6.2513, 'eval_samples_per_second': 3.199, 'eval_steps_per_second': 3.199, 'epoch': 0.36}
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{'loss': 0.944, 'learning_rate': 0.00019752490425051743, 'epoch': 0.38}
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{'loss': 1.0179, 'learning_rate': 0.00019705675197106016, 'epoch': 0.4}
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{'loss': 0.9346, 'learning_rate': 0.00019654872959986937, 'epoch': 0.41}
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{'loss': 0.9747, 'learning_rate': 0.0001960010458282326, 'epoch': 0.43}
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{'loss': 0.8928, 'learning_rate': 0.00019541392564000488, 'epoch': 0.45}
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{'loss': 0.9317, 'learning_rate': 0.00019478761021918728, 'epoch': 0.47}
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{'loss': 1.0534, 'learning_rate': 0.00019412235685085035, 'epoch': 0.49}
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{'loss': 0.8777, 'learning_rate': 0.00019341843881544372, 'epoch': 0.5}
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{'loss': 0.9447, 'learning_rate': 0.00019267614527653488, 'epoch': 0.52}
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{'loss': 0.9651, 'learning_rate': 0.00019189578116202307, 'epoch': 0.54}
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{'loss': 0.9067, 'learning_rate': 0.00019107766703887764, 'epoch': 0.56}
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```
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