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name: model-loading-inference description: "Load CLIP models, prepare images/text, and run safe image-text inference." disable-model-invocation: true metadata: disco-role: operating license: MIT

CLIP Model Loading and Inference

Use this sub-skill when an agent needs to import CLIP, choose a checkpoint, load the model/preprocess pair, prepare PIL images and tokenized text, or run image-text scoring and feature encoding.

Start Here

• For a no-download environment check, run python scripts/clip_smoke_check.py --json from this directory or call it by path from any project.
• For API details, model names, tensor shapes, Torch Hub entrypoints, and dtype/device expectations, read references/api-reference.md.
• For common loading and inference workflows, including local checkpoints and offline-safe validation, read references/workflows.md.
• For install/import, cache, checksum, JIT, CPU/GPU, preprocessing, and network failures, read references/troubleshooting.md.
• For a runnable one-image helper, use python scripts/image_text_similarity.py --help; it only downloads weights if the user runs it with a named model that is not already cached.

Scope Boundaries

This sub-skill owns model loading, cache/download choices, preprocessing, device/JIT placement, forward inference, logits/probabilities, and encode_image / encode_text usage.

Do not use this sub-skill for prompt template search, prompt ensembling, or label wording strategy; route those decisions to ../prompt-engineering/. Do not use it for dataset-scale embedding extraction, linear probes, or evaluation loops; route those workflows to ../feature-evaluation/.