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4-bit LLM quantization with AWQ v1.0.0

Use this resource to compress an LLM to 4 bits with AWQ for faster inference

New

  • 356 downloads
  • 119 likes
  • 205 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install awq-4bit-quantization

With no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.

First time: connect the CLI with

npx --yes palatekit@latest login

The agent installs AWQ, quantizes a model or loads a pre-quantized version, then configures generation. You get a lighter model for inference, with notes on hardware and accuracy.

Mostly picked by

Updated November 2, 2026

Card for the “4-bit LLM quantization with AWQ” skill

Details

Works in
Install guides
Generates with
Your agent's model
Licence
MIT

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What you get

  • SKILL.md: installation, Triton or CUDA kernels and model loading
  • references/advanced-usage.md: advanced quantization configuration
  • references/troubleshooting.md: common quantization errors

Good fit for

  • A 70B model must fit on a 24 GB GPU
  • You serve a chat model with vLLM and want lower latency

Not for

    Tags

    • awq
    • quantization
    • llm
    • inference

    About

    You say

    “Quantize our instruction model to 4-bit AWQ so we can serve it with vLLM on an RTX 4090.”

    Card for the “4-bit LLM quantization with AWQ” skill

    Skill published by davila7 under the MIT license, delivered in its original version.

    Show more

    How to use it

    1. Install the skill in your agent's skills folder
    2. Ask your agent to compress an LLM to 4 bits with AWQ for faster inference
    3. Review and adjust the result

    Content preview

    Files

    • LICENSE1.1 KB
    • NOTICE-PALATEKIT.md0.5 KB
    • references/advanced-usage.md8.1 KB
    • references/troubleshooting.md7.9 KB
    • SKILL.md8.5 KB

    Origin and licence

    Listed unmodified

    Author
    davila7
    Source
    github.com/davila7/claude-code-templates/tree/f57964ebda91cdfb31755661ecea9395f30020f1/cli-tool/components/skills/ai-research/optimization-awq
    Licence
    MIT

    Notice

    « optimization-awq » (davila7, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.

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