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hqq-quantization

Quantize LLMs to 4/3/2-bit precision without calibration data.

3|Updated Feb 21, 2026
Search Tags:#quantization#llm optimization#model compression#memory efficiency#inference speed#hqq
One-click install
npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill hqq-quantization-ihatesea69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hqq-quantization
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/10-optimization/hqq
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill hqq-quantization-ihatesea69

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of reducing the memory footprint and accelerating the inference speed of Large Language Models (LLMs) without requiring extensive calibration datasets, which are often difficult to obtain or computationally expensive.

Core Features & Use Cases

  • Calibration-Free Quantization: Quantize models to 4/3/2-bit precision without needing representative calibration data.
  • Fast Quantization Workflows: Significantly reduces the time required for quantization compared to methods like GPTQ or AWQ.
  • Optimized Inference: Supports various backends (vLLM, HuggingFace Transformers, Marlin, BitBlas) for efficient deployment.
  • Use Case: You have a large LLM like Llama-3.1-8B and need to deploy it on hardware with limited VRAM. This Skill allows you to quickly quantize the model to 4-bit, drastically reducing its size and enabling faster responses, all without needing a specific dataset for calibration.

Quick Start

Use the hqq-quantization skill to quantize the model 'meta-llama/Llama-3.1-8B' to 4-bit precision.

Frequently Asked Questions about hqq-quantization

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

FAQPage Schema
How do I quantize an LLM to 4-bit without needing calibration data?▼

You can achieve calibration-free quantization of Large Language Models to 4-bit, 3-bit, or 2-bit precision using the HQQ algorithm, which eliminates the need for representative datasets. This enables rapid model compression directly for memory efficiency and faster inference.

What is the fastest way to compress a Llama model for limited VRAM hardware?▼

The fastest way to compress models like Llama-3.1-8B for limited VRAM is using the HQQ algorithm for calibration-free quantization. It significantly reduces quantization time compared to methods like GPTQ or AWQ, drastically reducing model size for fast responses.

Can I fine-tune a quantized LLM using PEFT?▼

Yes, you can fine-tune quantized LLMs by integrating the HQQ quantization process with PEFT. This allows you to apply parameter-efficient fine-tuning techniques to models compressed to low bit-widths, maintaining memory efficiency while adapting the model.

Does HQQ quantization support deployment with vLLM and HuggingFace Transformers?▼

Yes, HQQ quantization supports deployment with both vLLM and HuggingFace Transformers, alongside optimized backends like Marlin and BitBlas. This facilitates efficient inference and diverse hardware deployment for your compressed models.

Why use calibration-free quantization instead of GPTQ or AWQ?▼

Calibration-free quantization is used instead of GPTQ or AWQ because it removes the need for extensive, computationally expensive calibration datasets. It provides a significantly faster quantization workflow while still achieving low bit-width compression for memory efficiency.

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