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name: single-modality description: "Operate InternVideo2 single-modality visual workflows for pretraining, finetuning, probing, distillation, model selection, datasets, launch adaptation, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating source-branch: InternVideo2-single_modality source-commit: 3965eef16e2dadd0ea6c8d0cc29c8a3039df52e3 license: Apache 2.0

InternVideo2 Single-Modality Operating Skill

Use this sub-skill when a user asks about the visual-only single_modality branch of InternVideo2: masked video pretraining, action-classification finetuning, linear or attentive probing, distillation from larger teachers, model-zoo choice, dataset/weight paths, SLURM or DeepSpeed launch adaptation, or single-modality failure diagnosis.

Do not use this sub-skill for video-text/audio retrieval, chat/video-MLLM work, InternVideo3, InternVideo-Next, or InternVid dataset acquisition beyond the action-recognition annotation layouts needed here. Route those to the appropriate sibling sub-skill or root repo guidance.

Operating protocol

1. Classify the requested workflow. Choose one of: pretraining, distillation, full finetuning, linear probing, attentive probing, or eval-only classification. Use references/workflows.md for entry points and command shapes.
2. Collect required user inputs before executing anything expensive. Confirm dataset split root or pretraining CSV, checkpoint/model root, desired model size, launcher (srun, torchrun, or plain python), GPU count, precision, and whether the user is asking for command construction only or an actual run.
3. Check prerequisites. Use references/configuration.md to verify Python/CUDA dependencies, INTERNVIDEO2_DATA_PATH, INTERNVIDEO2_MODEL_PATH, annotation CSV layout, teacher weight locations, and model registry names. Full training/evaluation requires user-provided datasets, checkpoints, CUDA-capable dependencies, and cluster resources.
4. Generate commands safely. Prefer the bundled helper scripts/build_single_modality_command.py to render a dry-run command. The helper prints command text only; it has no training side effects. Review paths, GPUs, batch size, DeepSpeed flags, and dataset class before running.
5. Adapt launchers deliberately. Source workflows are SLURM-first and usually use DeepSpeed with BF16. For local torchrun, preserve distributed environment variables and port selection. For single-process debugging, remove --enable_deepspeed, --dist_eval, and large batch sizes unless the user explicitly prepared that stack.
6. Diagnose by symptom. Use references/troubleshooting.md for missing CUDA extensions, teacher checkpoint failures, dataset parsing errors, stale source shell scripts, class-head mismatches, distributed hangs, and OOM recovery.

Quick route table

User intent	Use	Key inputs
Train Stage1 1B/6B visual model on K-Mash-style data	run_pretraining.py recipe in workflows	pretraining CSV, InternVL visual teacher, VideoMAEv2-g teacher, large multi-GPU launch
Distill S/B/L from InternVideo2 Stage2-1B	run_distill.py recipe in workflows	K-Mash CSV, teacher checkpoint registry patched to portable paths, model size
Full tune on Kinetics/K400/K600/K700/MiT/SSV2/ANet/HACS	run_finetuning.py recipe in workflows	split root with train.csv, val.csv, test.csv; checkpoint; classes; frames/crops
Linear probe or attentive probe	run_linear_probing.py recipe in workflows	checkpoint, dataset class, open_clip_projector/open_block_num, frame schedule
Select checkpoint/model family	configuration model table	target dataset, model scale, stage, available weights
Fix a run failure	troubleshooting	first error line, command, environment summary, dataset/checkpoint existence

Non-negotiable boundaries

• Do not submit source shell scripts blindly. They are cluster-specific and some contain stale names or non-portable checkpoint assumptions; distill their options into a reviewed command first.
• Do not claim CPU validation proves training readiness. The selected verification for this skill is static/helper-script verification; native training and evaluation remain optional heavyweight GPU/SLURM work.
• Do not expose private machine paths, local environment prefixes, or credentials in generated commands or reports.
• Do not modify user code to patch checkpoint paths or loader bugs without explicit user approval. If a patch is needed, explain the exact portable change and why.