---

name: model-inference description: "Load OpenCLIP, CLIP-style, and CoCa models for safe inference, tokenizer/preprocess setup, embeddings, and local or Hugging Face checkpoint routing." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Model Inference

Use this sub-skill when an agent needs to instantiate OpenCLIP models, select a pretrained tag or checkpoint source, prepare image/text inputs, create embeddings, or inspect model/tokenizer/preprocess configuration.

Route Here

• Load a built-in model by name with open_clip.create_model, open_clip.create_model_and_transforms, or open_clip.create_model_from_pretrained.
• Choose between random-init (pretrained=None), a named pretrained tag, a local checkpoint file, hf-hub:org/repo, or local-dir:/path model identifiers.
• Build tokenizer and image preprocess transforms with open_clip.get_tokenizer, open_clip.tokenize, open_clip.image_transform, or model-derived preprocess config.
• Run encode_image, encode_text, model(image=..., text=...), or CoCa feature/generation calls safely in eval/no-grad mode.
• Diagnose model loading, tokenizer, QuickGELU, optional dependency, device/precision, cache, and local-dir config failures.

Read First

• references/workflows.md for no-download inference, pretrained/HF/local loading, embeddings, CoCa caveats, and inspection recipes.
• references/api-reference.md for signatures, accepted identifier schemas, return contracts, and output shapes.
• references/model-selection.md for model/tag selection, QuickGELU compatibility, tokenizer choice, and local/HF checkpoint guidance.
• references/troubleshooting.md for common failures and precise fixes.
• scripts/inference_smoke.py for a deterministic helper that checks model creation, tokenizer, preprocess, and finite embeddings without downloads by default.

Quick Start

python sub-skills/model-inference/scripts/inference_smoke.py --model ViT-B-32 --pretrained none --device cpu

The smoke script defaults to pretrained=None, uses a generated image, and performs shape/finite checks only. Passing a named pretrained tag or hf-hub: model can trigger network/cache access; use those only when downloads are explicitly intended.

Boundaries

• Training loops, dataset ingestion, losses, EMA/FSDP, and checkpoint resume belong in ../training/SKILL.md.
• CLAP audio inputs and audio zero-shot workflows belong in ../audio-clap/SKILL.md.
• NaFlex patch dictionaries, GenLIP, and GenLAP workflows belong in ../naflex-generative/SKILL.md.
• Zero-shot classifier construction, retrieval scoring pipelines, checkpoint conversion, and export belong in ../evaluation-conversion/SKILL.md.