upload README.md

README.md

@@ -0,0 +1,55 @@
+---
+language: en
+license: gemma
+base_model: google/functiongemma-270m-it
+tags:
+  - coreml
+  - apple-neural-engine
+  - gemma3
+  - function-calling
+  - on-device
+library_name: coreml
+---
+
+# FunctionGemma-270M for Apple CoreML (ANE-optimized)
+
+CoreML conversion of `google/functiongemma-270m-it` produced with the
+[CoreML-LLM](https://github.com/john-rocky/CoreML-LLM) pipeline. Targets
+iOS 26 / macOS 26.
+
+## What's in this repo
+
+| File | Notes |
+|---|---|
+| `model.mlmodelc/` | Compiled stateful decoder (fp16, 840 MB). Drop-in for `MLModel(contentsOf:)` |
+| `model_config.json` | Bundle metadata (architecture, dims, function-call markers) |
+| `hf_model/` | Tokenizer + chat template (function-calling format) |
+| `cos_*.npy`, `sin_*.npy` | Pre-computed RoPE tables (optional) |
+
+## ANE residency
+
+**99.42% on Apple Neural Engine** (1893/1904 dispatched ops, verified via
+`MLComputePlan` on macOS 26). The 11 CPU-only ops are unavoidable
+input-boundary ops (token gather, argmax, scalar squeeze).
+
+## Use it
+
+Via the [CoreML-LLM Swift package](https://github.com/john-rocky/CoreML-LLM):
+
+```swift
+import CoreMLLLM
+let bundleURL = try await Gemma3BundleDownloader.download(
+    .functionGemma270m, into: appSupportDir)
+let fg = try await FunctionGemma.load(bundleURL: bundleURL)
+let text = try fg.generate(prompt: "Turn on the flashlight",
+                           maxNewTokens: 64)
+```
+
+For raw Core ML usage, the model expects the same I/O contract as Gemma 4:
+`input_ids (1,1) int32`, `position_ids (1,) int32`, `causal_mask (1,1,1,ctx) fp16`,
+`update_mask (1,1,ctx,1) fp16`, with a stateful `kv_cache_0` MLState
+(2*L, kv_heads, ctx, head_dim).
+
+## License
+
+Inherits Google's [Gemma terms of use](https://ai.google.dev/gemma/terms).