모델 Pruning
원문: model-pruning
Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, struc
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Model Pruning: Compressing LLMs When to Use This Skill Use Model Pruning when you need to: Reduce model size by 40 60% with <1% accuracy loss Accelerate inference using hardware friendly sparsity (2 4× speedup) Deploy on constrained hardware (mobile, edge devices) Compress without retraining using one shot methods Enable efficient serving with reduced memory footprint Key Techniques : Wanda (weights × activations), SparseGPT (second order), structured pruning, N:M sparsity Papers : Wanda ICLR 2024 (arXiv 2306.11695), SparseGPT (arXiv 2301.00774) Installation Quick Start Wanda Pruning (One Shot, No Retraining) Source : ICLR 2024 (arXiv 2306.11695) SparseGPT (Second Order Pruning) Source : arX…
실행 시 본인 API 키(BYOK)로 동작하며, 모델 비용은 사용자 계정에서 직접 결제됩니다.