Individual skill
tao-finetune-video-clip
InternVideo2-CLIP L14 (TAO video_clip) for video-text retrieval, zero-shot classification, embedding extraction, LoRA fine-tuning, ONNX export, and TensorRT deployment. Use when the user asks to "fine-tune IV2CLIP", "run video_clip train/evaluate/inference/export", "build a Video-CLIP TensorRT engine", "InternVideo2-CLIP on KPI chunks", or "TAO video_clip on vadr1_chunks JSON".
skills/tao-finetune-video-clip/SKILL.md
Source commit fce3cca4170be75e82c64920654d56ee2811b005
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- Network or broad shell
- Last checked
- Nov 9, 2026
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Repository context
- Repository stars (not skill ratings)
- 3,906
- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- agentic-ai Repository topic
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- video Declared skill tag
- vision-language Declared skill tag
- vlm Declared skill tag
- multimodal Declared skill tag
- retrieval Declared skill tag
- embedding Declared skill tag
- internvideo2 Declared skill tag
- iv2clip Declared skill tag
- fine-tuning Declared skill tag
- deployment Declared skill tag
- devops Derived category
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BENCHMARK.mddoc · 7,646 bytesSKILL.mdcore · 13,962 bytesSKILL.mdcore · 13,962 bytesSKILL.mdcore · 13,962 bytesSKILL.mdcore · 13,962 bytesSKILL.mdcore · 13,962 bytesSKILL.mdcore · 13,962 bytesSKILL.mdcore · 13,962 bytesSKILL.mdcore · 13,962 bytesSKILL.mdcore · 13,962 bytesSKILL.mdcore · 13,962 bytesSKILL.mdcore · 13,962 bytesconfig/skillspector-baseline.yamlasset · 1,508 byteseval.configasset · 9,985 bytesevals/evals.jsonasset · 4,864 bytesreferences/skill_info.yamlasset · 2,700 bytesreferences/spec_template_deploy_evaluate.yamlasset · 378 bytesreferences/spec_template_deploy_inference.yamlasset · 379 bytesreferences/spec_template_evaluate.yamlasset · 1,193 bytesreferences/spec_template_export.yamlasset · 792 bytesreferences/spec_template_gen_trt_engine.yamlasset · 395 bytesreferences/spec_template_inference.yamlasset · 1,255 bytesreferences/spec_template_train.yamlasset · 1,812 bytesreferences/tao-deploy-video-clip.mddoc · 4,096 bytesreferences/tao-deploy-video-clip.skill_info.yamlasset · 2,814 bytesskill-card.mddoc · 4,279 bytesskill.oms.sigasset · 7,629 bytes
Read excerpt
# InternVideo2-CLIP (TAO video_clip)
> **Standalone install?** If this session was not initialized by the TAO skill bank plugin, run the `tao-setup` skill first (host preflight, credentials, cross-skill discovery).
TAO task **`video_clip`** wraps OpenGVLab **InternVideo2-CLIP L14**. The PyTorch image provides `train`, `evaluate`, `inference`, `export`, and `default_specs`. TAO Deploy provides `gen_trt_engine`, TensorRT `evaluate`, and TensorRT `inference`.
Container images and per-action commands are in `references/skill_info.yaml` and `references/tao-deploy-video-clip.skill_info.yaml`. Starting specs are in `references/spec_template_*.yaml`.
> **Release note:** The pinned PyTorch image is the TAO 7.2 release-candidate build validated for Video-CLIP. It includes PyAV 17.1.0 as the primary decoder and ONNXScript 0.7.1 for export, with decord absent. The TAO Deploy image is pinned independently because `gen_trt_engine` and TensorRT-backed actions do not run in the PyTorch image.
>
> **Known-broken images:** interim builds cut before tao-pytorch commit `0cc31de4` ship a `video_clip` package with no `model.backbones` submodule, so `train`/`evaluate`/`inference` die at import while `video_clip --help` still exits 0. Images without PyAV also fail at data loading. Run both import checks in the preflight below before pulling data or launching a run.
## Train Action Policy
