liucongg/ChatGLM-Finetuning

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

2771 stars · 308 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python toolkit for fine-tuning ChatGLM-6B, ChatGLM2-6B, and ChatGLM3-6B large language models using Freeze, LoRA, P-Tuning, and full-parameter methods. It supports single- and multi-GPU training via DeepSpeed and includes evaluation for information extraction, generation, and classification tasks.

Use cases

• fine-tune chatglm on custom instruction data
• compare lora vs p-tuning vs freeze fine-tuning results
• train chatglm3-6b with deepspeed on multiple gpus
• adapt chatglm for information extraction tasks
• fine-tune a chinese llm on a single gpu with limited vram
• run full-parameter fine-tuning of chatglm2-6b

When to choose

• you specifically want to fine-tune ChatGLM-family models
• you want to experiment with and compare multiple PEFT methods (LoRA, P-Tuning, Freeze, full-parameter)
• you need DeepSpeed-based single- or multi-GPU training without the HuggingFace Trainer abstraction

When to avoid

• you use models other than ChatGLM/ChatGLM2/ChatGLM3
• you need actively maintained code for the latest model releases
• you want a production-ready training framework with broad model support

Facets

library · maturity maintenance

llm-training machine-learning deep-learning large-language-models machine-learning python chatglm lora p-tuning freeze deepspeed fine-tuning parameter-efficient-finetuning chinese-llm natural-language-processing gpu linux

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