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AI & LLM Engineering · CUDA · By amd
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Collect and normalize environment facts (OS, Python, GPU, CUDA/ROCm, container state) before Quark installation or PTQ planning. | amd/ | 182 | — | ~1.4k | Automated safety check: Pass | MIT | 13 days ago |
| 2 | Benchmarks LLM inference and drives GPU kernel optimization with Magpie. | amd/ | 408 | — | ~2.3k | Automated safety check: Pass | MIT | yesterday |
| 3 | 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. | amd/ | 182 | — | ~3.2k | Automated safety check: Pass | MIT | 13 days ago |
| 4 | Inspect a target ONNX model and prepare metadata for Quark ONNX PTQ planning. | amd/ | 182 | — | ~4.3k | Automated safety check: Pass | MIT | 13 days ago |
| 5 | Build a Quark ONNX PTQ quantization plan from modelanalysis.json and user intent. | amd/ | 182 | — | ~4.8k | Automated safety check: Pass | MIT | 13 days ago |
| 6 | Diagnose failed Quark installation, PTQ execution, script generation, or export attempts. | amd/ | 182 | — | ~1.9k | Automated safety check: Notes | MIT | 13 days ago |
| 7 | Install or verify the correct PyTorch build for a user's accelerator backend before Quark installation. | amd/ | 182 | — | ~1.6k | Automated safety check: Pass | MIT | 13 days ago |
