hqq-quantization SKILL.md skillA
hqq-quantization from yichen-zju/claude-for-research is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add yichen-zju/claude-for-research/hqq-quantization@v1.0.0Pins v1.0.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- yichen-zju/claude-for-research
- Path
skills/hqq/SKILL.md- Versions recorded
- 1
- Latest version
- v1.0.0, committed Sep 30, 2026
- Size
- 11,466 bytes
- Repository stars
- 10
- License
- none declared
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.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# 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) **Key advantages:** - **No calibration**: Quantize any model instantly without sample data - **Multiple backends**: PyTorch, ATEN, TorchAO, Marlin, BitBlas for optimized inference - **Flexible precision**: 8/4/3/2/1-bit with configurable group sizes - **Framework integration**: Native HuggingFace and vLLM support - **PEFT compatible**: Fine-tune quantized models with LoRA **Use alternatives instead:** - **AWQ**: Need calibration-based accuracy, production serving - **GPTQ**: Maximum accuracy with calibration data available - **bitsandbytes**: Simple 8-bit/4-bit without custom backends - **llama.cpp/GGUF**: CPU inference, Apple Silicon deployment ## Quick start ### Installation ```bash pip install hqq …
Read the whole file at its exact version.
How to install
mdr add yichen-zju/claude-for-research/hqq-quantization@sha256:4feba64227f6db50The line at the top of this page pins the label v1.0.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_hr767mqtbq2fiilz)
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Versions
The registry has recorded 1 version of this file, committed on Sep 30, 2026.
Audit of the latest version
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Source
yichen-zju/claude-for-research · 10 stars · license none declared · pushed 2026-10-21 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_hr767mqtbq2fiilz GET https://markdownregistry.com/api/v1/resolve?ref=yichen-zju/claude-for-research/hqq-quantization GET https://markdownregistry.com/api/v1/blob/4feba64227f6db505044663cd5432bf9c0599c5c8984d94d3b2d1598711a1f80
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Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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