Download
ChatGLM-Efficient-Tuning is a hands-on toolkit for fine-tuning ChatGLM-family models with parameter-efficient methods on everyday hardware. It wraps techniques like LoRA and prompt-tuning into simple training scripts so you can adapt a large model to your domain without full retraining. The project exposes practical switches for quantization and mixed precision, allowing bigger models to fit into limited VRAM. It includes examples for instruction tuning and dialogue datasets, making it straightforward to stand up a task-specific assistant. Because the code leans on widely used libraries, you can bring your own datasets and monitoring tools with minimal glue. For builders who want results fast, it’s a pragmatic way to specialize ChatGLM while controlling costs and turnaround time.
Features
- Parameter-efficient fine-tuning options such as LoRA and prompt-tuning
- Quantization and mixed-precision settings to reduce VRAM needs
- Ready-to-run scripts for instruction and conversation datasets
- Resume, evaluate, and export flows suitable for deployment
- Hooks for custom datasets and common logging backends
- Examples and defaults tuned for ChatGLM variants and typical GPUs
Project Samples
