Individual skill
quark-torch-llm-ptq
Runs an end-to-end AMD Quark post-training quantization workflow for PyTorch / Hugging Face LLMs: inspect a Hub or local model, choose a quantization plan, create reproducible artifacts, request execution approval, and produce a quantized model. Applies to Llama, Qwen, Mistral, and similar transformer LLM requests involving FP8, INT4, or another Quark scheme. Does not handle .onnx model inputs or ONNX PTQ.
skills/quark-torch-llm-ptq/SKILL.md
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- Last checked
- Nov 6, 2026
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.federated.jsonasset · 346 bytesSKILL.mdcore · 9,381 bytesSKILL.mdcore · 9,381 bytesSKILL.mdcore · 9,381 bytesSKILL.mdcore · 9,381 bytesevals/evals.jsonasset · 2,655 bytesreferences/contracts/model_analysis.schema.jsonasset · 1,890 bytesreferences/contracts/quant_plan.schema.jsonasset · 1,512 bytesreferences/contracts/run_manifest.schema.jsonasset · 1,662 bytesreferences/environment.mddoc · 3,816 bytesreferences/example-fp8-qwen3-8b.mddoc · 3,266 bytesreferences/model-intake.mddoc · 4,538 bytesreferences/quant-plan.mddoc · 4,789 bytesreferences/troubleshooting.mddoc · 5,116 bytesskill-card.mddoc · 214 bytes
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# Quark Torch PTQ
## Purpose
Take a PyTorch / Hugging Face LLM from model identification through confirmed
AMD Quark PTQ. Perform intake, planning, manifest generation, execution, and
output verification as one self-contained workflow.
This skill stops at the quantized model. It does not accept `.onnx` model input,
train or fine-tune a model, or modify Quark package/source files.
## Prerequisites
- Python 3.11 to 3.13, accelerator-matched PyTorch 2.2 or newer, `amd-quark[cli]`, and `datasets`.