skills/model-optimization/vllm/vllm-glm45-optimization/SKILL.md
PR-backed optimization manual for GLM-4.5 / 4.5V in vLLM. Use when an engineer needs to audit, debug, extend, or document GLM-4.5 text, GLM-4.5V, GLM-4.5-Air, shared MoE routing, and tool/reasoning parser behavior in vLLM.
npx skillsauth add BBuf/AI-Infra-Auto-Driven-SKILLS vllm-glm45-optimizationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill covers GLM-4.5 text, GLM-4.5V, GLM-4.5-Air, shared MoE routing, and tool/reasoning parser behavior in vLLM.
Evidence snapshot:
0f7be0f2f76814f80f9091220a5fbbb53912ad00references/pr-history.mdmodel-pr-optimization-history/vllm/glm45/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.
vllm/vllm/model_executor/models/glm4.pyvllm/vllm/model_executor/models/glm4_moe.pyvllm/vllm/model_executor/models/glm4v.pyModify the organization of GLM series: Reworked the family layout so 4.5-era models reused a cleaner GLM structure.not tie_word_embeddings for glm-4.5 and glm-4.5v: Aligned the loader with the real 4.5 checkpoint contract instead of forcing tied embeddings.Modify the gate implementation of glm4_moe: Changed the GLM4.5 MoE gating path used by text and VL variants.Add triton fused moe config for GLM-4.5-Air-FP8 on B200: Added a production kernel-tuning lane for the 4.5 Air FP8 deployment path.Add documentation for GLM-4.5 series tool-calling and reasoning parser: Codified the parser choices needed to serve 4.5 reasoning / tool checkpoints correctly.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.