AWQ 4-bit Model Quantization
Shrinks large language models to 4-bit weights for faster, lower-memory inference using AWQ.
- Repository
- Orchestra-Research/AI-Research-SKILLs
- Stars
- 14,195
- Licence
- MIT
- Files
- 3 files
What it does
Teaches the agent how to compress large language models (roughly 7B to 70B parameters) to 4-bit precision using Activation-aware Weight Quantization (AWQ), so they fit on smaller GPUs and run faster. It covers installing the autoawq library, loading pre-quantized models, quantizing your own model, choosing kernel backends such as GEMM, GEMV, Marlin and ExLlamaV2, and serving with vLLM. It also compares AWQ with GPTQ and bitsandbytes and lists memory, speed and perplexity figures.
Great for
- ML engineers deploying open-weight LLMs on limited GPU memory
- Developers serving quantized chat models in production with vLLM
- Teams comparing AWQ against GPTQ or bitsandbytes before choosing a quantization method
Skip it if
- Users without an NVIDIA or other supported GPU, since the guidance assumes CUDA hardware and the speedups are GPU-specific
- Anyone expecting zero accuracy change; the skill itself reports roughly 2-4% perplexity degradation, and results depend on the model and calibration data
- Non-technical users, because it requires running Python code and installing libraries
Try asking
- “Load a 4-bit AWQ version of Mistral 7B and generate text on my GPU”
- “Quantize my fine-tuned Llama model to 4-bit AWQ using my own calibration samples”
- “Set up vLLM to serve an AWQ-quantized model in production”
- “Explain the trade-offs between AWQ, GPTQ and bitsandbytes for my hardware”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 3 original files, the MIT licence and an attribution note.
awq.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip awq.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip awq.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Verify your GPU's compute capability and CUDA version before choosing Marlin or GEMM kernels.
- Benchmark perplexity or task accuracy on your own model after quantizing, since the published figures are examples.
- Pre-quantized community models on Hugging Face may come from third parties; check their provenance before running them.
What's inside
- references/advanced-usage.md
- references/troubleshooting.md
- SKILL.md
Redistributed unmodified from Orchestra-Research/AI-Research-SKILLs under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.