Download
LLM-Pruner is an open-source framework designed to compress large language models through structured pruning techniques while maintaining their general capabilities. Large language models often require enormous computational resources, making them expensive to deploy and inefficient for many practical applications. LLM-Pruner addresses this issue by identifying and removing non-essential components within transformer architectures, such as redundant attention heads or feed-forward structures. The framework relies on gradient-based analysis to determine which parameters contribute least to model performance, enabling targeted structural pruning rather than simple weight removal. After pruning, the framework applies lightweight fine-tuning methods such as LoRA to recover performance using relatively small datasets and short training times.
Features
- Structured pruning of transformer components such as layers and attention heads
- Gradient-based importance scoring for identifying removable parameters
- Compatibility with multiple LLM architectures including LLaMA and Vicuna
- Lightweight performance recovery using LoRA fine-tuning
- Automated scripts for pruning and model compression workflows
- Reduced memory usage and faster inference for large language models
Project Samples
