skills/model-optimization/sglang/sglang-gemma4-optimization/SKILL.md
PR-backed optimization manual for Gemma 4 in SGLang. Use when an engineer needs to audit, debug, extend, or document Gemma 4 text, MoE, multimodal, reasoning, tool use, and quantized MoE serving.
npx skillsauth add BBuf/AI-Infra-Auto-Driven-SKILLS sglang-gemma4-optimizationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
This skill covers Gemma 4 text, MoE, multimodal, reasoning, tool use, and quantized MoE serving.
Evidence snapshot:
c122d343adb969cd9bbd1af2ca86727a11be3845e88b0fd8ac5b1caa6eb42766035029220053369breferences/pr-history.mdmodel-pr-optimization-history/sglang/gemma4/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/gemma4_causal.pysglang/python/sglang/srt/models/gemma4_mm.pysglang/python/sglang/srt/models/gemma4_vision.pysglang/python/sglang/srt/models/gemma4_audio.pyNew Model: Gemma 4: Initial Gemma 4 support in SGLang.Gemma4 nvfp4 fix: Fixed the NVFP4 launch path.Adding Gemma 4 to Nightly CI: Added model-family regression coverage.development
Perform SGLang code review in the style of human maintainers by consulting the full non-agent PR review episode corpus from project start through the latest refresh (June 2026), including inline review threads, top-level PR comments, review submissions, original multilingual text, and multi-round discussions. Use when reviewing SGLang PRs, diffs, patches, or local changes for correctness, tests, performance, GPU/runtime risks, API compatibility, and maintainability.
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.
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.