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README.md

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+---
+license: other
+license_name: apache-2.0-and-gemma
+license_link: LICENSE.md
+base_model:
+- google/gemma-4-E4B-it
+- google/embeddinggemma-300m
+tags:
+- coreml
+- apple-neural-engine
+- on-device
+---
+
+# BubbleCrawler models
+
+The two Core ML models that [BubbleCrawler](https://github.com/wokenlex/bubblecrawler)
+downloads on first launch. The app runs them on the Apple Neural Engine.
+
+## Gemma4E4B
+
+gemma-4-E4B-it for the chunked engine of
+[john-rocky/CoreML-LLM](https://github.com/john-rocky/CoreML-LLM): four decode
+chunks plus batched prefill (N = 2048), context 4096.
+
+This conversion differs from `mlboydaisuke/gemma-4-E4B-coreml` in three ways:
+
+- **RoPE tables fixed.** `cos_full.npy` / `sin_full.npy` implement Gemma 4's
+  proportional RoPE for full attention: only the first 64 of the 256
+  frequencies rotate. Upstream rotates all of them, so the model loses track of
+  anything more than ~512 tokens back.
+- **Band mask for prefill.** Prefill applies the 512-token band mask to the
+  sliding-window layers.
+- **Multifunction chunks.** Each `chunkN.mlmodelc` holds two functions,
+  `decode_q1` (the default) and `prefill`, which share one copy of the weights.
+  The `verify_qK` functions are not included.
+
+The tokenizer adds no BOS token, so prepend `<bos>` yourself.
+
+## EmbeddingGemma
+
+EmbeddingGemma-300M from
+[erjigit17/embeddinggemma-300m-ane-coreml](https://huggingface.co/erjigit17/embeddinggemma-300m-ane-coreml),
+compiled to `model.mlmodelc`.
+
+- Inputs: `input_ids` and `attention_mask`, each int32 `[1, 128]`.
+- Output: `embedding`, `[1, 768]`.
+
+## Licenses
+
+- **Gemma4E4B:** Apache 2.0, the license of `google/gemma-4-E4B-it`.
+- **EmbeddingGemma:** Gemma is provided under and subject to the Gemma Terms of
+  Use found at [ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms).
+  Use is also subject to the
+  [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
+  See `EmbeddingGemma/NOTICE`.