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hqq-quantization

Quantize LLM weights to 8/4/3/2/1-bit precision without calibration.

11.5k|842|Updated Nov 3, 2025
Search Tags:#quantization#llm optimization#model compression#memory efficiency#inference speed#hqq
One-click install
npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill hqq-quantization
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hqq-quantization
Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/10-optimization/hqq
Command: npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill hqq-quantization

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of large language model (LLM) memory and computational requirements by enabling efficient weight quantization, making LLMs faster and more accessible on limited hardware.

Core Features & Use Cases

  • Calibration-Free Quantization: Quantize models to 4/3/2-bit precision without needing calibration datasets, significantly speeding up the quantization process.
  • Optimized Backends: Supports various backends (PyTorch, ATEN, TorchAO, Marlin, BitBlas) for optimized inference performance across different hardware.
  • Framework Integration: Seamlessly integrates with HuggingFace Transformers and vLLM for easy deployment and fine-tuning.
  • Use Case: Deploy a large LLM like Llama-3.1-8B on a consumer GPU by quantizing it to 4-bit using HQQ, enabling faster response times and reduced memory footprint for your application.

Quick Start

Use the hqq-quantization skill to quantize the 'meta-llama/Llama-3.1-8B' model 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 a large language model without a calibration dataset?▼

Calibration-free weight quantization reduces the memory footprint of large language models by quantizing weights to 8, 4, 3, 2, or 1-bit precision without requiring a calibration dataset. This accelerates the quantization process while preserving model integrity.

Can I deploy a 4-bit quantized LLM using vLLM and HuggingFace Transformers?▼

Yes, quantized LLMs can be deployed using vLLM and HuggingFace Transformers. The process integrates seamlessly with these frameworks, enabling efficient deployment and fine-tuning workflows directly after quantizing your model weights.

What is the best way to run Llama-3.1-8B on a consumer GPU with limited memory?▼

Quantizing models like Llama-3.1-8B to 4-bit precision using calibration-free weight quantization significantly reduces the memory footprint. This allows large language models to run on consumer GPUs, accelerating inference and enabling faster response times.

Does model quantization support optimized inference backends like Marlin and BitBlas?▼

Yes, model quantization supports multiple optimized backends including PyTorch, ATEN, TorchAO, Marlin, and BitBlas. These backends ensure optimized inference performance across different hardware environments after weight compression.

Why use calibration-free weight quantization instead of standard quantization methods?▼

Calibration-free weight quantization eliminates the need for calibration datasets, significantly speeding up the quantization process. It directly compresses large language models to lower bit precisions, reducing memory requirements while maintaining accelerated inference speeds.

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