HQQ Quantization

by Orchestra-Research

HQQ Quantization is an AI Engineering skill for Claude Code, published by Orchestra-Research in AI-Research-SKILLs.

11.6K stars844 forkson Orchestra-Research/AI-Research-SKILLsAdded 2026/07/14+1% in starsRepository updated 2026/06/16

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aiai-researchclaudeclaude-codeclaude-skillscodexgeminigpt-5grpohuggingfacemachine-leanringmegatronskillsvllm

Install in seconds

Install HQQ Quantization

Copy HQQ Quantization into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/10-optimization/hqq ~/.claude/skills/hqq

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/Orchestra-Research/AI-Research-SKILLs.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: 10-optimization/hqq/SKILL.md in Orchestra-Research/AI-Research-SKILLs
Installs to: ~/.claude/skills/hqq
Collection: One of 50 skills cataloged from this repository
Category: AI Engineering — 3670 skills

What HQQ Quantization does

HQQ Quantization applies calibration-free half-quadratic weight quantization to LLMs at 8/4/3/2/1-bit precision. Use it when quantizing models quickly, deploying with vLLM or HuggingFace Transformers, or fine-tuning quantized models with LoRA.

HQQ Quantization is cataloged under AI Engineering on DirSkills. HQQ Quantization comes from a repository tagged ai, ai-research, claude, claude-code and claude-skills.

Documentation

README

Excerpt

HQQ - Half-Quadratic Quantization

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

When to use HQQ

Use HQQ when:

Quantizing models without calibration data (no dataset needed)
Need fast quantization (minutes vs hours for GPTQ/AWQ)
Deploying with vLLM or HuggingFace Transformers
Fine-tuning quantized models with LoRA/PEFT
Experimenting with extreme quantization (2-bit, 1-bit)

This is the opening of the README. Read the full README on GitHub.

Frequently asked about HQQ Quantization

What else does Orchestra-Research publish alongside HQQ Quantization?
HQQ Quantization is one of 50 skills that DirSkills catalogs from Orchestra-Research/AI-Research-SKILLs, the repository it ships in. Its siblings there include AWQ Quantization, Autoresearch and Axolotl. Each one is a separate skill with its own page in this directory, installs the same way HQQ Quantization does, and is maintained by Orchestra-Research in that same repository. The rest of the collection is listed on the Orchestra-Research/AI-Research-SKILLs page.
How does HQQ Quantization compare to other AI Engineering skills?
HQQ Quantization ranks #338 by stars among the 3670 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of HQQ Quantization against them. Open each page to compare what they document and how they install.