Florence-2 — Core ML

Microsoft, 2024

Vision-language captioning, OCR, and VQA. Three-stage encoder-decoder. 768×768 input.

Florence-2 demo

Core ML conversion of microsoft/Florence-2 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	image text to text
Upstream	microsoft/Florence-2
Packages	3
Download size	229 MB
Minimum iOS	17.0
Peak RAM	~1200 MB

Files

File	Size	Compute units	SHA-256
`Florence2VisionEncoder.mlpackage.zip`	77 MB	`cpuOnly`	`9422f189c21220a0…`
`Florence2TextEncoder.mlpackage.zip`	69 MB	`cpuOnly`	`f985deeef0408ea8…`
`Florence2Decoder.mlpackage.zip`	81 MB	`cpuOnly`	`fe85a6faab528127…`
`florence2_vocab.json`	976 KB	`-`	`861fee9af5520403…`
Total	229 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

hf download mlboydaisuke/coreml-zoo --include "florence2/*" --local-dir ./florence2
unzip './florence2/florence2/*.zip' -d ./florence2

Use in 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 Florence2VisionEncoder(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 3 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/Florence2Demo, a standalone SwiftUI project.
Models Zoo — this model is downloadable and runnable inside the Models Zoo app on the App Store, no build required.

Conversion

Script: convert_florence2.py
Pitfalls hit during conversion (FP16 overflow, ANE buffer limits, stride handling): docs/coreml_conversion_notes.md
Model index: CoreML-Models