Environment:Intel Ipex llm CPU Finetuning Environment
| Knowledge Sources | |
|---|---|
| Domains | Infrastructure, LLM_Finetuning |
| Last Updated | 2026-02-09 04:00 GMT |
Overview
CPU-based environment for LoRA finetuning of LLMs using IPEX-LLM with HuggingFace Transformers, PEFT, and Datasets on Intel or compatible x86 processors.
Description
This environment provides a CPU-only context for LoRA finetuning of large language models using IPEX-LLM. It does not require a GPU and runs entirely on the CPU, making it suitable for systems without Intel discrete GPUs or for development and testing of finetuning workflows. The stack uses IPEX-LLM for model optimization, HuggingFace PEFT for LoRA adapter injection, HuggingFace Datasets for data loading, and the `fire` library for CLI argument parsing. This environment targets LoRA (not QLoRA) since CPU-based quantized training is limited.
Usage
Use this environment for CPU-based LoRA Finetuning workflows when Intel XPU hardware is not available or when testing finetuning pipelines on CPU. It is the mandatory prerequisite for running the IPEX-LLM CPU LoRA finetuning scripts.
System Requirements
| Category | Requirement | Notes |
|---|---|---|
| OS | Ubuntu 22.04 LTS or compatible Linux | Windows and macOS may also work |
| Hardware | x86_64 CPU (Intel recommended) | No GPU required; multi-core CPU recommended for performance |
| RAM | 32GB+ recommended | Model and training data must fit in system memory |
Dependencies
Python Packages
- `ipex-llm`
- `torch`
- `transformers`
- `peft`
- `datasets`
- `fire`
- `scipy`
- `accelerate`
Credentials
No credentials are required for local CPU finetuning. The following may optionally be needed:
- HuggingFace Model Access: If using gated models (e.g., Llama), a `HF_TOKEN` environment variable may be needed.
Quick Install
# Install IPEX-LLM (CPU-only, no XPU extras)
pip install --pre --upgrade ipex-llm
# Install finetuning dependencies
pip install transformers peft datasets fire scipy accelerate
Common Errors
| Error Message | Cause | Solution |
|---|---|---|
| `OutOfMemoryError` | Model too large for available system RAM | Use a smaller model or increase system RAM; consider using XPU environment instead |
| `ModuleNotFoundError: No module named 'peft'` | PEFT not installed | `pip install peft` |
| `ModuleNotFoundError: No module named 'fire'` | Fire CLI library not installed | `pip install fire` |
| `Slow training performance` | CPU-only training is inherently slower | Expected behavior; use XPU environment for production finetuning |
Compatibility Notes
- CPU Only: This environment runs entirely on CPU. For GPU-accelerated finetuning, use the XPU_Finetuning_Environment instead.
- LoRA Only: QLoRA (4-bit quantized training) is primarily designed for GPU. CPU finetuning typically uses standard LoRA with bf16 or fp32 precision.
- Performance: CPU finetuning is significantly slower than XPU finetuning. This environment is best suited for small models, development, and testing.