quark-onnx-ptq
SkillFiles & storageEnd-to-end ONNX PTQ workflow for AMD Quark — for `.onnx` input models (with optional sibling `.onnx_data` external-weights file). Use when the user wants a complete ONNX-to-ONNX pipeline: model intake, quantization planning, calibration-script generation, manifest, and confirmed execution. Trigger for "quantize my .onnx", "run ONNX PTQ end to end", "full ONNX quantization pipeline", "quantize yolov8/resnet50/yolo_nas with XINT8/A8W8/BFP16/MXFP*", "weights-only INT4 for my .onnx LLM", or any request that spans more than one ONNX PTQ step. Not for HuggingFace / safetensors / PyTorch input models — use quark-torch-ptq instead.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the quark-onnx-ptq skill
What this skill tells your AI
The instructions your AI receives, as published by amd/quark in .claude/skills/quark-onnx-ptq/SKILL.md and read by ahel’s review.
Read and follow the instructions in .claude/skills-impl/l2-workflows/onnx/quark-onnx-ptq-workflow/SKILL.md.
Signals
- GitHub stars
- 166
- Forks
- 33
- Last commit
- Aug 2026
Advanced
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- skill
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quark-onnx-ptq- Source
- github.com/amd/quark