This repository provides code for fine-tuning the ChatGLM-6b language model using Low-Rank Adaptation (LoRA). It targets researchers and developers looking to efficiently adapt large language models for specific tasks with reduced computational resources. The primary benefit is enabling fine-tuning on consumer-grade hardware, specifically mentioning an RTX 3090 with 24GB of GPU memory.

How It Works

The project leverages the LoRA technique, which injects trainable low-rank matrices into the existing model weights. This significantly reduces the number of trainable parameters (0.35% in the example) compared to full fine-tuning, leading to faster convergence and lower memory requirements. The code integrates with Hugging Face's transformers library and uses accelerate for multi-GPU and DeepSpeed-accelerated training, supporting ZeRO 2 and ZeRO 3 optimizations.

Quick Start & Requirements

• Install/Run: Use accelerate launch for training.
• Prerequisites: Python, PyTorch, transformers, loralib, accelerate, deepspeed. Minimum 24GB GPU memory (RTX 3090 recommended).
• Data Format: JSON list of {'prompt': ..., 'completion': ...} pairs.
• Docs: README

Highlighted Details

• Supports fine-tuning the entire model for potentially better performance.
• Includes options for multi-GPU training and DeepSpeed integration.
• Provides example data formatting and training loss curve visualization.
• Demonstrates saving and loading LoRA weights.

Maintenance & Community

The repository was last updated around April 2022. There is no explicit mention of active community channels or ongoing maintenance.

Licensing & Compatibility

The repository does not explicitly state a license. The code uses libraries with various licenses (e.g., Hugging Face transformers typically uses Apache 2.0). Users should verify compatibility for commercial or closed-source use.

Limitations & Caveats

The project appears to be from early 2022, and the ChatGLM-6b model itself might have been superseded. The lack of explicit licensing and recent activity could indicate limited ongoing support or potential compatibility issues with newer library versions.