clip-vit-base-patch32-CoreAI-official

Core AI is Apple's on-device ML runtime in iOS 27 / macOS 27 and the successor to Core ML: PyTorch models are exported with Apple's coreai-torch (LLMs: coreai.llm.export) into .aimodel bundles that run on the GPU or the Neural Engine, e.g. Qwen3-8B 4-bit decodes at 94 tok/s on an M4 Max GPU, MLX 90 under the same protocol (apple-silicon-llm-bench, macOS 27 beta 26A5353q, 2026-06-11).

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CLIP ViT-B/32 — Core AI export (official recipe)

fp16 static export of openai/clip-vit-base-patch32 via apple/coreai-models' official recipe (models/clip/export.py), with one change: text inputs are padded to the full 77-token context (padding="max_length") so free-text queries work, instead of the recipe's 7-token example trace.

Runs out of the box with CoreAIKit's ImageTextEncoder:

let encoder = try await ImageTextEncoder()   // downloads this repo
let imageVec = try await encoder.encode(image: cgImage)
let textVec  = try await encoder.encode(text: "red bike at the beach")
let score = ImageTextEncoder.cosineSimilarity(imageVec, textVec)

Bundle layout

model/
├── clip-vit-base-patch32_float16_static.aimodel
└── tokenizer.json

Graph contract

Preprocessing: 224×224 resize + CLIP mean/std normalization (handled by ImageTextEncoder).

Performance

M4 Max: ~3.7 ms per image on the Neural Engine (fp16). Requires macOS 27 / iOS 27 (device — the CoreAI framework is not in the iOS Simulator SDK).

License

Model weights: MIT (OpenAI CLIP); see the upstream repo. Export recipe: BSD-3-Clause (apple/coreai-models).

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More models in this format: Core AI Model Zoo — 75 models, each with the recipe that produced it.

Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.

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