gptq

What problem does it solve?

This Skill explains GPTQ post-training 4-bit quantization, enabling deployment of very large models (70B+) on consumer GPUs with reduced memory and competitive accuracy, plus integration with PEFT for QLoRA fine-tuning.

Core Features & Use Cases

• Memory reduction: 4-bit quantization achieves ~4× memory savings with minimal perplexity degradation.
• Faster inference: Quantized models run significantly faster than FP16 in many setups.
• Framework integration: Works with Transformers, AutoGPTQ, and PEFT for efficient fine-tuning.

Quick Start

Load a quantized model with AutoGPTQ, perform a forward pass, and compare performance against FP16.