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README.md

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+---
+license: apache-2.0
+base_model: google/siglip-base-patch16-224
+tags:
+  - vision
+  - onnx
+  - int8
+  - mobile
+  - flutter
+---
+
+# WakeUp SigLIP-1 Base INT8 (ONNX)
+
+ONNX INT8 exports of [`google/siglip-base-patch16-224`](https://huggingface.co/google/siglip-base-patch16-224) for use in the **WakeUp** Flutter alarm app's "Travel Mode" feature.
+
+## Files
+
+| File | Size | Purpose |
+|---|---|---|
+| `siglip1_image_encoder_int8.onnx` | ~99 MB | Image feature extraction (per-scan) |
+| `siglip1_text_encoder_int8.onnx` | ~111 MB | Text feature extraction (Custom Text mode only) |
+| `model_metadata.json` | — | `logit_scale`, `logit_bias`, normalization params |
+| `tokenizer/` | — | SigLIP-1 tokenizer files |
+
+## Scoring
+
+Both encoders L2-normalize their output. SigLIP scoring is:
+
+```
+logits = exp(logit_scale) * cosine(image_emb, text_emb) + logit_bias
+prob   = sigmoid(logits)
+```
+
+Constants from `model_metadata.json`:
+- `logit_scale = 4.765` (so `exp(scale) ≈ 117.33`)
+- `logit_bias = -12.932`
+
+## Inference
+
+```python
+import onnxruntime as ort
+import numpy as np
+
+img_sess = ort.InferenceSession("siglip1_image_encoder_int8.onnx")
+txt_sess = ort.InferenceSession("siglip1_text_encoder_int8.onnx")
+
+# image: 1x3x224x224 normalized with mean/std [0.5, 0.5, 0.5]
+image_emb = img_sess.run(None, {"pixel_values": pixel_values})[0]
+# text: input_ids + attention_mask (use all-ones mask for canonical inference)
+text_emb = txt_sess.run(None, {"input_ids": ids, "attention_mask": np.ones_like(ids)})[0]
+
+cos = text_emb @ image_emb.T
+prob = 1 / (1 + np.exp(-(np.exp(4.765) * cos + -12.932)))
+```
+
+## License
+
+Apache 2.0 (inherits from base model).