skills/model-optimization/vllm/vllm-minimax-optimization/SKILL.md
PR-backed optimization manual for MiniMax M1 / M2 / VL in vLLM. Use when an engineer needs to audit, debug, extend, or document MiniMaxText01, MiniMax-M1, MiniMax-M2, MiniMax-VL-01, LoRA, and Eagle3 support in vLLM.
npx skillsauth add BBuf/AI-Infra-Auto-Driven-SKILLS vllm-minimax-optimizationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill covers MiniMaxText01, MiniMax-M1, MiniMax-M2, MiniMax-VL-01, LoRA, and Eagle3 support in vLLM.
Evidence snapshot:
0f7be0f2f76814f80f9091220a5fbbb53912ad00references/pr-history.mdmodel-pr-optimization-history/vllm/minimax/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/minimax_text_01.pyvllm/vllm/model_executor/models/minimax_m2.pyvllm/vllm/model_executor/models/minimax_vl_01.pySupport MiniMaxText01 model inference: Landed the original MiniMax text runtime.support MiniMax-VL-01 model: Added the multimodal MiniMax-VL path.Add support for MiniMaxM1ForCausalLM: Connected the M1 checkpoint alias to the shared MiniMax runtime.Support MiniMax-M2 Model: Brought the M2 generation into mainline.Complete LoRA support for MiniMaxM2: Finished missing adapter wiring in the M2 family.MiniMax-M2: add Eagle3 speculative decoding support: Enabled the draft-model acceleration path for MiniMax M2.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.