ipex-llm/python/llm/example/GPU/LLM-Finetuning

Running LLM Finetuning using IPEX-LLM on Intel GPU

This folder contains examples of running different training mode with IPEX-LLM on Intel GPU:

• LoRA: examples of running LoRA finetuning
• QLoRA: examples of running QLoRA finetuning
• QA-LoRA: examples of running QA-LoRA finetuning
• ReLora: examples of running ReLora finetuning
• DPO: examples of running DPO finetuning
• common: common templates and utility classes in finetuning examples
• HF-PEFT: run finetuning on Intel GPU using Hugging Face PEFT code without modification
• axolotl: LLM finetuning on Intel GPU using axolotl without writing code

Verified Models

Model	Finetune mode	Frameworks Support
LLaMA 2/3	LoRA, QLoRA, QA-LoRA, ReLora	HF-PEFT, axolotl
Mistral	LoRA, QLoRA	DPO
ChatGLM 3	QLoRA	HF-PEFT
Qwen-1.5	QLoRA	HF-PEFT
Baichuan2	QLoRA	HF-PEFT

Troubleshooting

• If you fail to finetune on multi cards because of following error message:

  RuntimeError: oneCCL: comm_selector.cpp:57 create_comm_impl: EXCEPTION: ze_data was not initialized
  

  Please try sudo apt install level-zero-dev to fix it.

• Please raise the system open file limit using ulimit -n 1048576. Otherwise, there may exist error Too many open files.

• If application raise wandb.errors.UsageError: api_key not configured (no-tty). Please login wandb or disable wandb login with this command:

export WANDB_MODE=offline

• If application raise Hugging Face related errors, i.e., NewConnectionError or Failed to download etc. Please download models and datasets, set model and data path, then set HF_HUB_OFFLINE with this command:

export HF_HUB_OFFLINE=1