hqq-quantization

Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

Skills
AI research
0

What it does and how to use it

A skill that teaches Claude Code to do ai research tasks well. It is packaged knowledge the model uses whenever it is relevant.

Install the skill (command above).
In Claude Code, describe what you need - the skill turns on by itself when the context fits.
No commands to memorize: you talk in plain language and the skill does the rest.

Install (Claude Code)

mkdir -p .claude/skills/hqq-quantization && curl -fsSL https://doutorai.pt/data/lib/skills__hqq-quantization.md -o .claude/skills/hqq-quantization/SKILL.md

Paste this command into the terminal, in your project folder, and the component installs automatically.

Content

---
name: hqq-quantization
description: Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Quantization, HQQ, Optimization, Memory Efficiency, Inference, Model Compression]
dependencies: [hqq>=0.2.0, torch>=2.0.0]
---

# HQQ - Half-Quadratic Quantization

Fast, calibration-free weight quantization supporting 8/4/3/2/1-bit precision with multiple 

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