awq-quantization SKILL.md skillA
This file is byte-identical to the first copy the registry indexed, one of 3 indexed copies. Same content hash, so the same checks give both the same grade.
awq-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/awq-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
claude/skills/awq/SKILL.md- Versions recorded
- 1
- Latest version
- v1.0.0, committed Oct 14, 2026
- Size
- 8,383 bytes
- Repository stars
- 10
- License
- none declared
Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.
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
# AWQ (Activation-aware Weight Quantization) 4-bit quantization that preserves salient weights based on activation patterns, achieving 3x speedup with minimal accuracy loss. ## When to use AWQ **Use AWQ when:** - Need 4-bit quantization with <5% accuracy loss - Deploying instruction-tuned or chat models (AWQ generalizes better) - Want ~2.5-3x inference speedup over FP16 - Using vLLM for production serving - Have Ampere+ GPUs (A100, H100, RTX 40xx) for Marlin kernel support **Use GPTQ instead when:** - Need maximum ecosystem compatibility (more tools support GPTQ) - Working with ExLlamaV2 backend specifically - Have older GPUs without Marlin support **Use bitsandbytes instead when:** - Need zero calibration overhead (quantize on-the-fly) - Want to fine-tune with QLoRA - Prefer simpler integration ## Quick start ### Installation ```bash # Default (Triton kernels) pip install autoawq # With optimized CUDA kernels + Flash Attention pip install autoawq[kernels] # Intel CPU/XPU optimization pip install autoawq[cpu] ``` **Requirements**: Python 3.8+, CUDA 11.8+, Compute Capability 7.5+ ### Load pre-quantized model ```python from awq import AutoAWQForCausalLM …
Read the whole file at its exact version.
How to install
mdr add yichen-zju/claude-for-research/awq-quantization@sha256:9a875e53113c11b0The 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_76fb42yi2uwzh6zg)
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Versions
The registry has recorded 1 version of this file, committed on Oct 14, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (8383 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
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_76fb42yi2uwzh6zg GET https://markdownregistry.com/api/v1/resolve?ref=yichen-zju/claude-for-research/awq-quantization GET https://markdownregistry.com/api/v1/blob/9a875e53113c11b00ab417fc393eb504f8be97d50c2e6901c762b991388bf717
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
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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