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awq-quantization

Quantize large language models to 4-bit using activation-aware weight quantization.

6|3|Updated Jan 29, 2026
Search Tags:#quantization#model compression#inference speed#activation-aware#large language model
One-click install
npx skills add https://github.com/jonnabio/ace-framework --skill awq-quantization-jonnabio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: awq-quantization
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/ai-research/awq
Command: npx skills add https://github.com/jonnabio/ace-framework --skill awq-quantization-jonnabio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires autoawq, transformers, torch, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides 4-bit activation-aware weight quantization, enabling the compression of large language models while maintaining high inference speed and minimal accuracy loss.

Core Features & Use Cases

  • Activation-aware Quantization: Preserves salient weights based on activation patterns.
  • Speedup: Achieves up to 3x inference speedup over FP16.
  • Accuracy: Maintains accuracy within 5%.
  • Use Case: Ideal for deploying large models on limited GPU memory with faster inference and better accuracy preservation than GPTQ.

Quick Start

Use the autoawq package to quantize your model. Example command: pip install autoawq

Frequently Asked Questions about awq-quantization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compress a large language model for faster inference on limited GPU memory?▼

Activation-aware quantization preserves salient weights based on activation patterns, achieving up to 3x inference speedup over FP16 while maintaining accuracy within 5%.

How does activation-aware quantization compare to other methods like GPTQ?▼

Activation-aware quantization preserves salient weights based on activation patterns, providing better accuracy preservation and faster inference than GPTQ for limited GPU memory deployments.

Can I use the autoawq package with transformers and torch to quantize my model?▼

Yes, this activation-aware quantization process uses the autoawq package alongside transformers and torch dependencies to compress large language models and accelerate inference.

What is the accuracy trade-off when applying 4-bit weight quantization to large models?▼

When applying 4-bit weight quantization, the accuracy loss is minimal, maintaining performance within 5% of the original FP16 model while achieving significant compression and speedup.

When should I avoid using 4-bit activation-aware weight quantization?▼

You should avoid 4-bit activation-aware quantization if your deployment lacks GPU memory constraints or if your use case cannot tolerate up to a 5% drop in model accuracy.

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