Fine-Tuning Large Language Models on Intel Xeon CPUs

This technical report provides a comprehensive guide for fine-tuning large language models (LLMs) using Intel Xeon CPUs, particularly focusing on the 5th Gen Intel Xeon processors. It outlines the growing need for CPU-based fine-tuning due to the high costs and limited availability of GPUs. The report details a practical implementation using the Lenovo ThinkSystem SR650 V3 server, optimized for AI workloads. It includes prerequisites such as knowledge of Python, Linux, and Hugging Face libraries, and offers a step-by-step workflow for fine-tuning a Llama3.2-1B model on the Alpaca QA dataset. Key processes include setting up a virtual environment, loading the base model, dataset pre-processing, and configuring LoRA adapters for efficient training. The report also discusses optimizations for Intel Advanced Matrix Extensions (AMX) and validates the effectiveness of CPU-based fine-tuning through performance evaluations. This resource is aimed at researchers and engineers looking to efficiently fine-tune LLMs while minimizing reliance on expensive GPU infrastructure.