# mymusise/ChatGLM-Tuning

基于ChatGLM-6B + LoRA的Fintune方案

Repository: https://github.com/mymusise/ChatGLM-Tuning
Canonical: https://ross.abutalabs.com/products/chatglm-tuning
Language: Python
License: MIT
License Family: permissive
Topics: chatglm, chatgpt, lora, peft
Archived: true
Last push: 2023-11-25T08:46:31+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 90
- inputs: {"age_days": 1266, "days_push": 1012, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3740, forks 434 (observed 2026-08-28T04:08:15.944211+00:00)

## What it is
A Python toolkit for fine-tuning the ChatGLM-6B large language model using LoRA (Low-Rank Adaptation) with the Alpaca dataset. It provides data preprocessing, tokenization, and training scripts runnable on consumer GPUs with 16GB+ VRAM, including Colab notebooks.

## Use cases
- fine-tune chatglm-6b on custom instruction data
- train a cheap open-source chatgpt alternative with lora
- convert alpaca dataset to l for finetuning
- run llm finetuning on a single consumer gpu
- try peft lora tuning of chatglm in colab

## When to choose
- you want to fine-tune ChatGLM-6B or ChatGLM2 with LoRA on limited hardware
- you need a simple script-based pipeline for instruction tuning with the Alpaca dataset

## When to avoid
- you need full-parameter finetuning or RLHF/PPO training, which is not implemented
- you use models other than ChatGLM v1/v2
- you need an actively maintained project with recent updates

## Facets
- artifact type: library
- maturity: maintenance
- function: llm-training, machine-learning, deep-learning
- domain: large-language-models, machine-learning, deep-learning
- platform: python
- tags: chatglm, lora, peft, finetuning, alpaca, colab, gpu, linux

## Member repositories
- mymusise/ChatGLM-Tuning (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:15.944211+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:29:07.768575+00:00, confidence not recorded.
  - readme: https://github.com/mymusise/ChatGLM-Tuning (fetched 2026-08-28T04:08:15.944211+00:00, sha 8d2bdda0567b)
- Data as of 2026-08-30T08:39:29.467469+00:00.