This project implements various fine-tuning approaches for ChatGLM foundation models, including parameter freezing (Freeze), low-rank adaptation (LoRA), prefix tuning (P-tuning), and full-parameter fine-tuning. It supports single-GPU and multi-GPU training configurations with gradient checkpointing and zero optimization for memory efficiency. The repository focuses on downstream tasks such as information extraction, text generation, and classification.