model-pruning

What problem does it solve?

This Skill tackles the challenges of deploying large language models, which are often slow and memory-intensive. It enables you to significantly reduce model size and accelerate inference, making LLMs more practical for edge devices and cost-effective serving.

Core Features & Use Cases

• Model Compression: Reduce LLM size by 40-60% with minimal accuracy loss (typically <1%), making models lighter and easier to store.
• Inference Acceleration: Achieve 2-4× speedup in inference on hardware accelerators by leveraging structured sparsity patterns like N:M pruning.
• One-Shot Pruning: Compress models without the need for extensive and costly retraining, using efficient methods like Wanda and SparseGPT.
• Efficient Deployment: Enable deployment on resource-constrained hardware (e.g., mobile, edge devices) and reduce the memory footprint for serving.
• Use Case: Deploy a Llama-2-7b model on a mobile device or a low-cost GPU server, achieving faster response times and lower operational costs without sacrificing much performance.

Quick Start

Apply Wanda pruning to a Llama-2-7b-hf model to achieve 50% sparsity using a small calibration dataset, without any retraining.