skills/model-optimization/sglang/sglang-intern-s1-optimization/SKILL.md
PR-backed optimization manual for Intern-S1 in SGLang. Use when an engineer needs to audit, debug, extend, or document Intern-S1 language and video-aware serving, processor integration, and tool/reasoning parser behavior.
npx skillsauth add BBuf/AI-Infra-Auto-Driven-SKILLS sglang-intern-s1-optimizationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill covers Intern-S1 language and video-aware serving, processor integration, and tool/reasoning parser behavior.
Evidence snapshot:
c122d343adb969cd9bbd1af2ca86727a11be3845e88b0fd8ac5b1caa6eb42766035029220053369breferences/pr-history.mdmodel-pr-optimization-history/sglang/intern-s1/README.zh.md and README.en.mdUse skills/model-optimization/model-pr-diff-dossier/SKILL.md as the production bar.
Every PR cited for this family must be based on diff reading, not only PR titles.
sglang/python/sglang/srt/models/interns1.pysglang/python/sglang/srt/models/internvl.pyInternS1 image token updates in InternVL processor: Aligned the shared processor with Intern-S1 image semantics.Fix Intern-S1 accuracy and /generate input_ids support: Closed early correctness gaps.Add tool calling and reasoning parser support for Intern-S1: Added parser support that cookbook usage depends on.Support InternS1 text_config in InternVL processor: Improved sub-config compatibility in shared processors.development
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.
devops
Inspect LLM torch profiler traces at forward-pass, layer, and kernel level. Use when you need layer timings, anchor-kernel boundaries, representative kernel flows, or Perfetto time ranges.
development
Run an autonomous Humanize-governed SGLang SOTA performance loop for one LLM model: first perform the fixed fair SGLang/vLLM/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 SGLang code, optionally uses ncu-report-skill for kernel evidence, and revalidates until SGLang matches or beats the best observed framework under the same workload and SLA.
documentation
Use when an SGLang, vLLM, or TensorRT-LLM serving/model optimization task needs prior model-family PR evidence. Query and read the PR-driven history docs under model-pr-optimization-history before choosing source paths, fast paths, kernel/fusion ideas, regression risks, or validation lanes.