LLM Model Pruning

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This skill provides a comprehensive framework for reducing the size and computational requirements of Large Language Models (LLMs) through state-of-the-art pruning techniques. It enables developers to implement one-shot methods like Wanda and SparseGPT, achieving up to 50% sparsity with negligible accuracy loss. Whether you are deploying models on edge devices, optimizing for NVIDIA sparse tensor cores with N:M structured pruning, or seeking to lower inference latency in production, this skill offers the implementation patterns and best practices needed to balance model performance with resource efficiency.

Características Principales

• One-shot pruning with Wanda and SparseGPT for rapid compression
• Support for N:M semi-structured sparsity (2:4, 4:8) for hardware acceleration
• Customizable layer-wise and iterative pruning strategies
• Accuracy-aware pruning achieving <1% loss at 50% sparsity
• 384 GitHub stars
• Implementation of both structured and unstructured pruning methods

Casos de Uso

• Deploying large transformer models on mobile or edge devices with limited VRAM
• Reducing cloud hosting costs by decreasing the memory footprint of served LLMs
• Accelerating inference throughput on NVIDIA A100/H100 GPUs using sparse tensor cores