quark-env-preflight

Étoiles152

Forks26

Mis à jour9 juillet 2026 à 06:10

Collect and normalize environment facts (OS, Python, GPU, CUDA/ROCm, container state) before Quark installation or PTQ planning. Trigger for "check my environment", "what GPU do I have", "is my setup ready for Quark", or when any accelerator-related assumption is unconfirmed. Also trigger before any install/quantization step where hardware facts are missing.

Installation

Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.

Exécuter dans Manus

Source

amd

amd/Quark

Ouvrir le dépôt GitHub Voir les dépôts du créateur

Téléchargement

Exécuter dans Manus

Métiers associésSOC

Basé sur la classification professionnelle SOC

Administrateurs de réseaux et de systèmes informatiquesProfessions informatiques et mathématiques·SOC 15-1244

SKILL.md

readonly

name	quark-env-preflight
description	Collect and normalize environment facts (OS, Python, GPU, CUDA/ROCm, container state) before Quark installation or PTQ planning. Trigger for "check my environment", "what GPU do I have", "is my setup ready for Quark", or when any accelerator-related assumption is unconfirmed. Also trigger before any install/quantization step where hardware facts are missing.

Read and follow the instructions in .claude/skills-impl/l0-foundation/shared/quark-env-preflight/SKILL.md.

Plus depuis ce dépôt

même dépôt

quark-env-preflight

amd/Quark

Collect and normalize environment facts (OS, Python, GPU, CUDA/ROCm, container state) before Quark installation or PTQ planning. Use this skill whenever a downstream skill needs confirmed hardware and toolchain facts, when the user mentions their setup, when you need to decide between CUDA/ROCm/CPU install paths, or when any accelerator-related assumption is unconfirmed. Also trigger when the user says things like "check my environment", "what GPU do I have", "is my setup ready for Quark", or before any install/quantization step where hardware facts are missing.

2026-07-09152

quark-workspace-validate

amd/Quark

Validate workspace shape, model paths, output directories, and repo structure before downstream Quark skills proceed. Use this skill whenever a skill needs confirmed file paths, when the user provides a model path or output directory, when you need to distinguish a local model from a HuggingFace ID, or when any path assumption is unverified. Also trigger for "where should I put outputs", "is my model path right", or before model-intake/export steps.

2026-07-09152

quark-onnx-debug

amd/Quark

Diagnose failed Quark ONNX installation, calibration, quantization, custom-op compilation, or export attempts. Use when the user reports an error, stack trace, invalid artifact, missing dependency, ORT execution-provider mismatch, silent CPU fallback, OOM during calibration, custom-op load failure (BFPQuantizeDequantize / MXQuantizeDequantize / Extended*), or unexpected quantization results from the ONNX flow. Trigger for "Quark ONNX error", "onnxruntime error", "quantize_static failed", "calibration crashed", "CUDAExecutionProvider not available", "ROCMExecutionProvider not available", "custom op library load failed", "model.onnx larger than 2GB", "external data not found", "AdaRound diverged", "GPTQ ONNX failed", "QuaRot failed", "NPU power-of-2 scale", any Python traceback mentioning quark.onnx / onnxruntime / onnx, or when the user pastes an error message related to Quark ONNX workflows.

2026-07-09152

quark-onnx-install

amd/Quark

Install or verify the correct ONNX Runtime build (and the `onnx` package) for a user's accelerator backend before Quark's ONNX-to-ONNX flow. Use when the user needs ONNX Runtime set up, reports onnxruntime version conflicts, CPU vs GPU variant mix-ups (only one variant of `onnxruntime*` may be installed at a time), missing CUDA/ROCm execution providers, or when `import onnxruntime` / `import onnx` fails. Trigger for "install onnxruntime", "pip install onnxruntime", "set up onnxruntime for ROCm", "set up onnxruntime for CUDA", "onnxruntime-gpu vs onnxruntime", "onnx version mismatch", "CPU-only onnxruntime installed", "ORT providers list missing CUDAExecutionProvider/ROCMExecutionProvider", or any request to get the correct ONNX Runtime build running. Also trigger when `quark-install` reports that ONNX Runtime is missing or mismatched before proceeding with the ONNX flow.

2026-07-09152

quark-onnx-model-intake

amd/Quark

Inspect a target ONNX model and prepare metadata for Quark ONNX PTQ planning. Use when the user needs `.onnx` path validation, opset / IR version detection, input/output shape and dtype discovery, op-type histogram, quantizable-op counting, deployment-target compatibility checks (CPU / CUDA / ROCm / AMD NPU CNN / AMD NPU Transformer), or risk assessment before planning PTQ. Trigger for "analyze my ONNX model", "check this onnx model", "what opset is this", "can Quark quantize this .onnx", "is my model NPU-compatible", "does my model already have QDQ", "is my model larger than 2 GB", or when any ONNX quantization step needs model facts that are missing.

2026-07-09152

quark-onnx-quant-plan

amd/Quark

Build a Quark ONNX PTQ quantization plan from `model_analysis.json` and user intent. Use when the user needs preset selection (XINT8 / A8W8 / A16W8 / BF16 / BFP16 / MX* / MXFP* …), calibration method choice (MinMax / Entropy / Percentile / Distribution / NonOverflow / MinMSE / LayerWisePercentile), algorithm selection (CLE / AdaRound / AdaQuant / BiasCorrection / AutoMixprecision), deployment-target gating (CPU / CUDA / ROCm / AMD NPU CNN / AMD NPU Transformer), op-type include/exclude lists, weights-only INT4 (MatMulNBits) decisions, dynamic vs static quantization, or a draft `quant_plan.json`. Trigger for "what preset should I use for my ONNX model", "choose XINT8 vs A8W8", "plan ONNX PTQ", "INT4 "BFP16 / MXFP4 for my model", "calibration method for Ryzen AI", "should I use AdaRound or AdaQuant", "SmoothQuant alpha", or when the user has a model analysis and needs to decide how to quantize an ONNX model.

2026-07-09152