model-pruning

Compress a large language model for faster inference by applying advanced pruning techniques like Wanda and SparseGPT. These methods reduce model size by 40-60% and accelerate inference speeds by 2-4x without significant accuracy loss.

The best way to prune an LLM without retraining is using one-shot compression methods. This skill supports one-shot compression via Wanda and SparseGPT, enabling significant model size reduction without the need for iterative fine-tuning.

N:M sparsity accelerates LLM inference on constrained hardware by creating hardware-friendly sparsity patterns. This structured pruning approach reduces memory footprint and computational requirements, enabling deployment on edge devices for real-time processing.

Yes, you can use Wanda and SparseGPT to achieve both unstructured and structured pruning. The skill supports both methods, offering hardware-friendly N:M sparsity patterns alongside unstructured compression for optimal accuracy.

Yes, you need PyTorch and Hugging Face Transformers to prune a llama-2-7b model. The skill relies on dependencies including torch, transformers, accelerate, and datasets to execute the model compression and evaluate accuracy.

A limitation of one-shot LLM pruning for edge deployment is the potential for accuracy degradation compared to iterative approaches with fine-tuning. While one-shot compression avoids retraining, iterative methods deliver optimal accuracy for resource-constrained hardware.