Add model card

README.md

@@ -0,0 +1,93 @@
+---
+license: apache-2.0
+library_name: coreml
+pipeline_tag: zero-shot-image-classification
+base_model: google/siglip-base-patch16-224
+base_model_relation: quantized
+tags:
+  - coreml
+  - core-ml
+  - ios
+  - macos
+  - apple
+  - on-device
+  - siglip
+  - clip
+  - zero-shot
+  - image-text
+  - arxiv:2303.15343
+---
+
+# SigLIP — Core ML
+
+*Zero-Shot Classification, 2023*
+
+Zero-shot image classification. Dual encoder (image + text). 224×224 input.
+
+Core ML conversion of [google-research/big_vision](https://github.com/google-research/big_vision) for on-device inference on iPhone, iPad and Mac. Converted with `coremltools`; the packages are stateless, so all sequencing and buffering lives in your Swift code.
+
+| | |
+|---|---|
+| Task | zero shot image classification |
+| Upstream | [google-research/big_vision](https://github.com/google-research/big_vision) |
+| Packages | 2 |
+| Download size | 358 MB |
+| Minimum iOS | 17.0 |
+| Peak RAM | ~800 MB |
+
+## Files
+
+| File | Size | Compute units | SHA-256 |
+|---|---:|---|---|
+| `SigLIP_ImageEncoder.mlpackage.zip` | 162 MB | `cpuOnly` | `98f6abf5f4aa1451…` |
+| `SigLIP_TextEncoder.mlpackage.zip` | 195 MB | `cpuOnly` | `9dead2d58705838a…` |
+| `siglip_vocab.json` | 658 KB | `-` | `b94b3a58e04f6199…` |
+| **Total** | **358 MB** | | |
+
+`compute_units` is not a suggestion -- it is the configuration the conversion was verified against. Moving a package to a different compute unit can silently change the numerics (FP16 attention overflow) or crash on the GPU.
+
+## Download
+
+```bash
+hf download mlboydaisuke/coreml-zoo --include "siglip/*" --local-dir ./siglip
+unzip './siglip/siglip/*.zip' -d ./siglip
+```
+
+## Use in Swift
+
+```swift
+import CoreML
+
+let config = MLModelConfiguration()
+config.computeUnits = .cpuOnly   // as converted — see the table above
+
+// Unzip the .mlpackage, drop it into your Xcode target and Xcode compiles it
+// at build time:
+let model = try SigLIP_ImageEncoder(configuration: config)
+
+// ...or compile a downloaded .mlpackage at runtime:
+let compiled = try await MLModel.compileModel(at: mlpackageURL)
+let model = try MLModel(contentsOf: compiled, configuration: config)
+```
+
+> This model is split into 2 Core ML packages that are driven in sequence from Swift. Load them one at a time, copy the outputs out of the `MLMultiArray` buffers and release each model before loading the next — two large Core ML models resident at once will OOM on an iPhone.
+
+## Demo
+
+- **Sample app** — [`sample_apps/SigLIPDemo`](https://github.com/john-rocky/CoreML-Models/tree/master/sample_apps/SigLIPDemo), a standalone SwiftUI project.
+- **Models Zoo** — this model is downloadable and runnable inside the [Models Zoo app](https://apps.apple.com/app/id6762083207) on the App Store, no build required.
+
+## Conversion
+
+- Script: [`convert_siglip.py`](https://github.com/john-rocky/CoreML-Models/blob/master/conversion_scripts/convert_siglip.py)
+- Pitfalls hit during conversion (FP16 overflow, ANE buffer limits, stride handling): [`docs/coreml_conversion_notes.md`](https://github.com/john-rocky/CoreML-Models/blob/master/docs/coreml_conversion_notes.md)
+- Model index: [CoreML-Models](https://github.com/john-rocky/CoreML-Models)
+
+## License
+
+The conversion inherits the upstream license: **Apache-2.0**.
+
+## Credits
+
+- Upstream authors: [google-research/big_vision](https://github.com/google-research/big_vision), 2023
+- Core ML conversion: john-rocky (Daisuke Majima)