Which GPUs are supported by this skill?

GPTQ works on most modern NVIDIA GPUs. Specific kernels like Marlin are optimized for Ampere and newer architectures (A100, RTX 30/40 series) for maximum performance.

How does GPTQ compare to AWQ?

GPTQ is a well-established method with broad model support and excellent speed via the ExLlamaV2 kernel. AWQ may offer slightly better accuracy on specific newer architectures but often requires more specific hardware support.

What is the main benefit of GPTQ?

GPTQ reduces LLM memory requirements by approximately 4x and increases inference speed by 3-4x while maintaining nearly the same accuracy as standard FP16 precision.

Where can I find pre-quantized models?

Many pre-quantized GPTQ models are available on the Hugging Face Hub, with creators like 'TheBloke' providing thousands of ready-to-use quantized model files.

Can I use GPTQ for fine-tuning?

Yes, GPTQ integrates with the PEFT library to enable QLoRA, allowing you to fine-tune large models like Llama-70B on significantly less VRAM.

GPTQ Model Optimization

Name: GPTQ Model Optimization
Author: zechenzhangAGI

byzechenzhangAGI

•

384

Ciencia de Datos y ML

Optimizes Large Language Models using 4-bit post-training quantization to reduce memory usage and accelerate inference on consumer GPUs.

Acerca de

The GPTQ skill provides a comprehensive toolkit for implementing 4-bit quantization on Large Language Models (LLMs) like Llama 3, Mistral, and DeepSeek. By leveraging group-wise quantization and Hessian-based error minimization, it enables the deployment of massive models (up to 405B) on limited GPU hardware, achieving a 4x reduction in memory footprint and up to 4.8x speedup with minimal accuracy loss. This skill is essential for researchers and engineers looking to fine-tune models via QLoRA or run high-performance inference using specialized backends like ExLlamaV2 and Marlin.

Características Principales

Flexible group-wise quantization configurations to balance speed, memory, and accuracy
384 GitHub stars
4-bit post-training quantization with minimal (<2%) accuracy loss
Accelerated inference via high-performance kernels including ExLlamaV2 and Marlin
Support for massive models like Llama 3 (70B/405B) on consumer-grade GPUs
Seamless integration with Hugging Face Transformers and PEFT for QLoRA fine-tuning

Casos de Uso

Accelerating production inference throughput by 3-4x compared to FP16 precision
Deploying a 70B parameter model on a single 80GB A100 or 24GB consumer GPU
Fine-tuning large models using QLoRA on budget-friendly hardware

Acerca de

Características Principales

Flexible group-wise quantization configurations to balance speed, memory, and accuracy
384 GitHub stars
4-bit post-training quantization with minimal (<2%) accuracy loss
Accelerated inference via high-performance kernels including ExLlamaV2 and Marlin
Support for massive models like Llama 3 (70B/405B) on consumer-grade GPUs
Seamless integration with Hugging Face Transformers and PEFT for QLoRA fine-tuning

Casos de Uso

Accelerating production inference throughput by 3-4x compared to FP16 precision
Deploying a 70B parameter model on a single 80GB A100 or 24GB consumer GPU
Fine-tuning large models using QLoRA on budget-friendly hardware