skills/model-optimization/sglang/sglang-qwen3-coder-optimization/SKILL.md
PR-backed and current-main optimization manual for Qwen3-Coder and Qwen3-Coder-Next in SGLang. Use when an engineer needs to recover, extend, or audit Qwen3-Coder-480B-A35B, Qwen3-Coder-Next, tool-call parser behavior, incremental streaming tool arguments, NVFP4/FP8 loading, MoE fused configs, AMD/NPU/Blackwell recipes, or coding-agent deployment docs.
npx skillsauth add BBuf/AI-Infra-Auto-Driven-SKILLS sglang-qwen3-coder-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.
Qwen3-Coder needs its own skill because its optimization surface spans both model execution and parser behavior. The parser is used by Qwen3.6 docs as well, so changes here can affect multiple Qwen deployments.
Current evidence snapshot:
origin/main: b3e6cf60a on 2026-04-22origin/main: 816bad5 on 2026-04-21python/sglang/srt/function_call/qwen3_coder_detector.pydocs_new/cookbook/autoregressive/Qwen/Qwen3-Coder.mdx, Qwen3-Coder-Next.mdx, and their deployment snippetsUse 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.
Capture:
Separate parser correctness from model performance.
python/sglang/srt/function_call/qwen3_coder_detector.pydocs_new/cookbook/autoregressive/Qwen/Qwen3-Coder.mdxdocs_new/cookbook/autoregressive/Qwen/Qwen3-Coder-Next.mdxdocs_new/src/snippets/autoregressive/qwen3-coder-deployment.jsxdocs_new/src/snippets/autoregressive/qwen3-coder-next-deployment.jsxpython/sglang/srt/models/qwen3_next.pypython/sglang/srt/models/qwen3_moe.pyUse references/pr-history.md as the authoritative ledger. It includes manually reviewed merged PRs and manually reviewed open design PRs. If you cite another Qwen3-Coder PR, first open its metadata and full diff, then add a dossier entry before using it as evidence.
Open PRs that are already diff-reviewed in the ledger:
--tool-call-parser qwen3_coder.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.