---

name: tinyclip description: "Routes TinyCLIP OpenCLIP-style model inspection, zero-shot evaluation, and weight-inheritance pretraining workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

TinyCLIP

Use this sub-skill when the user is working with TinyCLIP model configs, inference, evaluation, or pretraining. It covers the OpenCLIP-style Python API and the stage-based training / evaluation commands documented in the repo.

What this route owns

• Model config discovery and model/tokenizer construction through open_clip.
• Zero-shot ImageNet evaluation and inference-style workflows.
• Manual and auto weight-inheritance pretraining stage commands.

When to use it

Choose this route for prompts like:

• "inspect TinyCLIP model configs"
• "run TinyCLIP zero-shot evaluation"
• "build the inference command for TinyCLIP"
• "prepare TinyCLIP pretraining stages"
• "fix a TinyCLIP open_clip import or checkpoint issue"

What to read next

• references/api-reference.md for the verified open_clip API surface.
• references/workflows.md for inference, evaluation, and pretraining command shapes.
• references/troubleshooting.md for install, checkpoint, data-loader, and distributed issues.
• scripts/inspect_tinyclip_models.py to list TinyCLIP model configs or instantiate one through open_clip.
• scripts/build_tinyclip_command.py to print safe launcher templates.

Important boundaries

• Do not route TinyViT, MiniViT, EfficientViT, or generic vision-only CLIP-style tasks here.
• Treat the original stage shell scripts as documentation for the workflow, not as runtime dependencies.
• Keep references self-contained; do not point back to the source checkout for model configs or stage notes.

Working pattern

1. Determine whether the user wants inference / evaluation or a pretraining stage.
2. Read the API reference for the open_clip constructors and model-config names.
3. Use the inspection script when you need to list configs or verify that a model can be instantiated.
4. Use the command-builder script when the user wants a reproducible launcher string.

Common signals

• open_clip is the import name for the installed open_clip_torch distribution.
• TinyCLIP-... model names are provided by the JSON configs under src/open_clip/model_configs/.
• The zero-shot path uses src/training/main_for_test.py with --imagenet-val, --model, --eval, and --resume.