vllm-sota-humanize-loop
Skill4 starsChanged 3 months agoNo licenceView on GitHub
Run an autonomous Humanize-governed vLLM SOTA performance loop for one LLM model: first perform the fixed fair vLLM/SGLang/TensorRT-LLM deployment search and benchmark, then start one RLCR loop that repeatedly decides the gap, profiles the current bottleneck, runs layer/kernel pipeline analysis, patches vLLM code, optionally uses ncu-report-skill for kernel evidence, and revalidates until vLLM matches or beats the best observed framework under the same workload and SLA.
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62 rules. About 0 are common, 33 are distinctive, and 29 are very specific to this project.
≈0
Common · estimate
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≈33
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29
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