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name: video-mllm description: "Operate InternVideo2.5 and InternVideo3 video MLLM inference, SFT, evaluation, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

InternVideo video MLLM operating skill

Use this sub-skill when the task is about InternVideo2.5 or InternVideo3 video multimodal large language models: model choice, Hugging Face loading, text/image/video message construction, long-video reasoning, InternVideo3 SFT command shaping, benchmark evaluation readiness, or video-MLLM troubleshooting.

Do not use this sub-skill for InternVideo2 visual-only or video-text retrieval training, InternVideo-Next visual pretraining, or dataset schema validation except as a handoff to the sibling datasets sub-skill.

Route by task

• Quick inference or demo: read references/inference.md first. It contains distilled model-loading and message-shape templates for InternVideo3 plus selection notes for InternVideo2.5 LRC/HiCo models.
• InternVideo3 SFT/CPT setup: read references/internvideo3-sft.md, then use the datasets sub-skill to validate meta JSON and JSONL annotations before constructing a launch command.
• Benchmark planning: read references/evaluation.md and classify each evaluation as GPU/data-heavy unless the user only asks for command inspection.
• Failures: read troubleshooting.md before changing dependencies, cache directories, frame limits, or evaluation data roots.

Operating guardrails

1. Treat all full model runs, SFT jobs, and benchmark submissions as optional/unverified until the user provides checkpoints, benchmark data, storage, CUDA/FlashAttention readiness, and launch approval.
2. Use placeholders such as <model-id-or-dir>, <annotation-meta.json>, and <work-dir> in advice; do not leak local environment paths.
3. Prefer distilled command shapes over telling the user to run repository shell scripts. The source scripts are cluster- and dependency-specific and may install packages at runtime.
4. Distinguish inference chat schemas from SFT JSONL schemas: Hugging Face processor messages use type: "video" / type: "image"; InternVideo3 SFT JSONL uses type: "video_url" / type: "image_url" with <VIDEO_CONTEXT> / <IMG_CONTEXT> placeholders.

Expected handoff

When done, report: selected generation/model, required runtime inputs, chosen message or SFT/eval schema, command shape or validation action, backend/data caveats, and unresolved gaps. Never claim that a heavy training or benchmark result was verified unless it was actually executed in the user-approved environment.