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SkillsMP a collecté 46 skills depuis amd/Quark. Ouvrez un skill pour examiner sa source et ses détails.
Affichage de 40 skills collectés sur 46.
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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 /…
Langue du texte source : anglais
Validate Quark ONNX quantization output using four lightweight checks: auxiliary file copy alignment, expected non-quantized initializer MD5 byte-identity (inline `raw_data` + external-data byte ranges), model metadata equality after stripping…
Langue du texte source : anglais
Route Quark ONNX user goals to the correct atomic skill. Use when a user describes an ONNX quantization task in plain language — such as "install onnxruntime", "analyze my .onnx model", "choose a preset for my YOLO model", "plan ONNX PTQ", "quantize this…
Langue du texte source : anglais
Install or verify the AMD Quark package and its dependencies. Use when the user needs Quark package installation, dependency setup, or post-install verification — after PyTorch is already set up. Trigger for "install Quark", "set up Quark", "pip install…
Langue du texte source : anglais
Diagnose failed Quark installation, PTQ execution, script generation, or export attempts. Use when the user reports an error, stack trace, invalid artifact, missing dependency, CUDA OOM, version mismatch, or unexpected PTQ results. Trigger for "Quark error",…
Langue du texte source : anglais
Prepare export and downstream evaluation handoff for a planned or completed Quark PTQ run. Use when the user wants to export a quantized model to HuggingFace SafeTensors, ONNX, or GGUF format, package for deployment, or set up post-quantization evaluation.…
Langue du texte source : anglais
Low-memory file2file quantization for very large safetensors LLMs that cannot be loaded whole. Use when the user wants to run file2file quantization, adapt a new safetensors checkpoint without loading the full model, register an external LLMTemplate, inspect…
Langue du texte source : anglais
Install or verify the correct PyTorch build for a user's accelerator backend before Quark installation. Use when the user needs PyTorch set up, reports torch version conflicts, CUDA/ROCm package mismatches, or when torch.cuda.is_available() returns False.…
Langue du texte source : anglais
End-to-end LLM accuracy evaluation on AMD ROCm (ROCm-only) — container setup, vLLM/SGLang/ATOM serving, lm-eval / lighteval / evalscope benchmarks. Use when the user wants to evaluate, benchmark, or compare an LLM's accuracy. Trigger for "evaluate this…
Langue du texte source : anglais
Inspect a target model and prepare metadata for Quark PTQ planning. Use when the user needs model path validation, architecture detection, quantization target discovery, layer counting, risk assessment, or transformer compatibility checks before planning PTQ.…
Langue du texte source : anglais
Build a Quark Torch LLM PTQ quantization plan from model analysis and user intent. Use when the user needs quantization scheme recommendations, exclusion lists, algorithm selection, KV cache decisions, per-layer overrides, or a draft quant_plan. Trigger for…
Langue du texte source : anglais
Validate Quark quantization output using four lightweight checks: auxiliary file copy alignment, excluded tensor MD5 byte-identity, config.json deep comparison after stripping quantization keys, and safetensors header pattern/dtype summaries. Intended for…
Langue du texte source : anglais
Route Quark user goals to the correct atomic skill or workflow. Use when a user describes a Quark task in plain language — such as "install Quark", "quantize a model", "analyze my model", "build a PTQ plan", "export the quantized model", "debug a failed run",…
Langue du texte source : anglais
End-to-end ONNX PTQ workflow for AMD Quark — from a `.onnx` file (and calibration data) to a quantized `.onnx` output. Use when the user wants a complete ONNX-to-ONNX PTQ pipeline: model intake, quantization planning, calibration-script generation, manifest,…
Langue du texte source : anglais
Torch LLM PTQ workflow for AMD Quark — from model selection to quantized output. Use when the user wants a complete PTQ pipeline: model inspection, quantization planning, script generation, and optional execution. Stops at the quantized output. Trigger for…
Langue du texte source : anglais
L3 recipe that runs `quark.onnx.AutoSearchPro` end-to-end on a user `.onnx` model: intake → preset selection (or custom search space) → calibration / eval data reader → standalone autosearch script generation → confirmed execution → best_params reporting.…
Langue du texte source : anglais
L3 recipe that runs a Torch LLM PTQ end-to-end for AMD Quark — for PyTorch / HuggingFace transformers models (safetensors input): quantize → validate → evaluate. Phase 1 delegates the full PTQ path (model intake → quantization planning → manifest generation →…
Langue du texte source : anglais
Compare ONNX skill contracts and guidance against current Quark ONNX documentation and source entry points. Use when maintainers need to verify that ONNX install docs, custom-op registry, QConfig fields, preset and calibration lists, AutoSearchPro presets,…
Langue du texte source : anglais
Manually verify that the Quark ONNX skill family behaves correctly across the four contract categories (routing, planning, artifact, recovery). Use when maintainers need to confirm that ONNX routing, planning, artifact generation, or error recovery skills…
Langue du texte source : anglais
Detect upstream Quark ONNX changes that affect the ONNX skill family and classify required updates. Use when Quark ONNX docs, custom-op registry, quantization config presets, calibration methods, AutoSearchPro presets, ONNX Runtime install matrix, or source…
Langue du texte source : anglais
Author or restructure a Quark Agent Skill so it conforms to this project's template, contracts, and layer rules. Use when a maintainer says "create a new skill", "add a skill for X", "scaffold a skill", "draft a SKILL.md", or when an existing skill needs a…
Langue du texte source : anglais
Compare skill contracts and guidance against current Quark documentation and source entry points. Use when maintainers need to verify that installation docs, artifact schemas, PTQ planning assumptions, CLI flags, or supported model lists still match upstream…
Langue du texte source : anglais
Manually verify that the Quark Agent Skills system behaves correctly across the four contract categories (routing, planning, artifact, recovery). Use when maintainers need to confirm that routing, planning, artifact generation, or error recovery skills still…
Langue du texte source : anglais
Detect upstream Quark changes that affect the skill system and classify required updates. Use when Quark docs, CLI flags, quantization templates, model support, or source behavior may have drifted from the skill contracts. Trigger for "check if skills are up…
Langue du texte source : anglais
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…
Langue du texte source : anglais
Install or verify the AMD Quark package and its dependencies. Trigger for "install Quark", "set up Quark", "pip install amd-quark", dependency errors, import failures for quark modules, ModuleNotFoundError for quark, or missing C++ compiler errors. For…
Langue du texte source : anglais
End-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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
Install or verify the correct ONNX Runtime build (and the matching `onnx` package) for a user's accelerator backend before Quark ONNX-flow usage. Trigger for "install onnxruntime", "pip install onnxruntime", "set up onnxruntime for ROCm", "set up onnxruntime…
Langue du texte source : anglais
Inspect a target ONNX model and prepare metadata for Quark ONNX PTQ planning. Use for `.onnx` path validation, opset / IR version detection, input-output shape and dtype discovery, op-type histogram, quantizable-op counting, deployment-target compatibility…
Langue du texte source : anglais
End-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,…
Langue du texte source : anglais
Validate Quark ONNX quantization output using four lightweight checks: auxiliary file copy alignment, expected non-quantized initializer MD5 byte-identity (inline `raw_data` + external-data byte ranges), model metadata equality after stripping…
Langue du texte source : anglais
Diagnose failed Quark Torch PTQ installation, execution, script generation, or export attempts. Use when the user reports a torch-side error, stack trace, invalid artifact, missing dependency, CUDA OOM, version mismatch, or unexpected PTQ results. Trigger for…
Langue du texte source : anglais
Prepare export and downstream evaluation handoff for a planned or completed Quark Torch PTQ run. Input is a PyTorch / HuggingFace transformers model; output formats include HF safetensors, GGUF, and ONNX. Trigger for "export model", "save quantized model",…
Langue du texte source : anglais
Low-memory file2file quantization for very large safetensors LLMs that cannot be loaded whole. Use when the user wants to run file2file quantization, adapt a new safetensors checkpoint without loading the full model, register an external LLMTemplate, inspect…
Langue du texte source : anglais