skills/model-optimization/vllm/vllm-mimo-v2-flash-optimization/SKILL.md
PR-backed optimization manual for MiMo-V2 / MiMo-V2-Flash / MiMo-V2.5 in vLLM. Use when an engineer needs to audit, debug, extend, or document MiMo-V2 inference-centric MoE runtime, MTP behavior, MiMo-V2.5 Pro/Omni support, and the transition from older MiMo checkpoints in vLLM.
npx skillsauth add BBuf/AI-Infra-Auto-Driven-SKILLS vllm-mimo-v2-flash-optimizationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill covers the MiMo-V2 family in vLLM: historical MiMo-V2-Flash,
current mimo_v2.py runtime, MTP behavior, MiMo-V2.5-Pro, MiMo-V2.5-Omni, and
the transition from older MiMo checkpoints.
Evidence snapshot:
origin/main: fd74c90d9 on 2026-04-27#40967 added MiMo-V2.5 Pro and Omni support, moving
the active runtime surface to mimo_v2.py, mimo_v2_mtp.py, and
mimo_v2_omni.py while retaining the historical Flash architecture alias.references/pr-history.mdmodel-pr-optimization-history/vllm/mimo-v2-flash/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/mimo_v2.pyvllm/vllm/model_executor/models/mimo_v2_mtp.pyvllm/vllm/model_executor/models/mimo_v2_omni.pyvllm/vllm/model_executor/models/mimo_v2_flash.pyvllm/vllm/model_executor/models/mimo.pyvllm/vllm/model_executor/models/mimo_mtp.pyMiMoV2FlashForCausalLM and MiMoV2ProForCausalLM to
mimo_v2, with MiMo-V2.5 Omni handled through mimo_v2_omni.Support MiMo-7B inference with MTP: Historical base for the MiMo family.Fix MTP inference path for MiMo-7B model: Closed a concrete draft-path bug.Add MiMo-V2-Flash support: Landed the dedicated V2-Flash runtime.use diff kv backend for mimo v2 flash: Added the DiffKV attention backend path.Add MiMo-V2.5 support: Added Pro, Omni, MTP, registry, and tests/docs coverage for the current family.references/pr-history.md: diff-reviewed MiMo-V2 family PR cards; it includes historical Flash-file cards and current MiMo-V2.5 updates.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.