Add model card

README.md

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+---
+license: mit
+base_model: openai/clip-vit-base-patch32
+pipeline_tag: feature-extraction
+library_name: coreai
+tags:
+  - core-ai
+  - ios
+  - clip
+  - embeddings
+---
+
+# CLIP ViT-B/32 encoders for Core AI
+
+[`openai/clip-vit-base-patch32`](https://huggingface.co/openai/clip-vit-base-patch32) exported to Apple Core AI as one `.aimodel` with separate text and image encoders, so text and images embed on their own into the same space.
+
+Apple's [`models/clip`](https://github.com/apple/coreai-models/tree/main/models/clip) recipe traces one function that needs an image and a fixed batch of three prompts on every call, so this export uses [`export.py`](./export.py), based on it (coreai-torch 0.4.3, torch 2.13):
+
+```sh
+uv run --script export.py --output-dir exports --dtype float16
+```
+
+| Folder                    | Contents                                                                                                                                                                                                                                                   | Size   | Platforms  |
+| ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------ | ---------- |
+| `clip-vit-base-patch32/`  | `clip-vit-base-patch32.aimodel` with `encode_text(input_ids, attention_mask) -> text_embeds` (`[batch, 77]` int32) and `encode_image(pixel_values) -> image_embeds` (`[batch, 3, 224, 224]` float16, CLIP mean and std), batch 1–64, 512-dim L2-normalized embeddings. `tokenizer/`: the CLIP tokenizer | 290 MB | iOS, macOS |
+
+## Use
+
+With [`@react-native-ai/core-ai`](https://github.com/callstackincubator/ai/tree/main/packages/core-ai), download `clip-vit-base-patch32/` into app storage, then embed text with the AI SDK and images with the native API:
+
+```ts
+import { coreAI, CoreAI } from '@react-native-ai/core-ai'
+import { cosineSimilarity, embedMany } from 'ai'
+
+const model = coreAI.embeddingModel({
+  id: 'clip-vit-base-patch32',
+  source: { type: 'file', uri: pathToFolder },
+})
+const { embeddings } = await embedMany({ model, values: ['a photo of a cat', 'a photo of a car'] })
+const { embeddings: [image] } = await model.embedImages([imageUri])
+
+cosineSimilarity(embeddings[0], image) // higher for the matching caption
+```
+
+## License
+
+CLIP is MIT licensed. This repository only converts it to Core AI format.