From Scratch: Large Language Model Development and Fine-Tuning with PyTorch and ChatGLM, from Local Deployment to Advanced LoRA and PEFT Fine-Tuning

The story

This article has 1.2w views, 23 likes, and 20 saves. It systematically explains local deployment of the ChatGLM model, including source code downloads, archive merging, and half precision loading. It focuses on three efficient fine tuning techniques: full fine tuning with Accelerator, LoRA low rank adapter fine tuning (including custom implementation and quantization acceleration), and parameter efficient fine tuning with Hugging Face PEFT. It covers data preprocessing (triplet extraction), mixed precision training, INT4/INT8 quantization, Adapter and Prompt Tuning comparisons, and emphasizes reproducibility, version control, and closed loop evaluation in engineering practice.