quark-onnx-autosearch-pro
SkillSearchEnd-to-end Quark ONNX AutoSearchPro recipe — drives `quark.onnx.AutoSearchPro` (Optuna-based hyperparameter search) on a `.onnx` model to find the best quantization config (activation/weight spec, calibration method, CLE, AdaRound / AdaQuant, FastFinetune params). Use when the user wants to "auto search", "tune quantization", "find the best quant config", "sweep AdaRound/AdaQuant", "run AutoSearchPro / AutoSearch", "two-stage search", or pick one of the built-in presets (`ADVANCED_SEARCH`, `XINT8_SEARCH`, `A8W8_SEARCH`, `A16W8_SEARCH`) for their `.onnx` model. Not for HuggingFace / safetensors / PyTorch input models — use quark-torch-ptq instead. Not for single-shot ONNX PTQ without search — use quark-onnx-ptq.
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-autosearch-pro skill
What this skill tells your AI
The instructions your AI receives, as published by amd/quark in .claude/skills/quark-onnx-autosearch-pro/SKILL.md and read by ahel’s review.
Read and follow the instructions in .claude/skills-impl/l3-recipes/onnx/quark-onnx-autosearch-pro/SKILL.md.
Signals
- GitHub stars
- 166
- Forks
- 33
- Last commit
- Aug 2026
Advanced
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- skill
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quark-onnx-autosearch-pro- Source
- github.com/amd/quark