metadata
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 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:
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.