I have a MacBook Pro M3 Pro with 18GB of RAM and was following the instructions to fine tune the foundational model given here: https://developer.apple.com/apple-intelligence/foundation-models-adapter/ However, while following the code sample in the example Jupyter notebook, my Mac hangs on the second code cell. Specifically: from examples.generate import generate_content, GenerationConfiguration from examples.data import Message output = generate_content( [[ Message.from_system("A conversation between a user and a helpful assistant. Taking the role as a play writer assistant for a kids' play."), Message.from_user("Write a script about penguins.") ]], GenerationConfiguration(temperature=0.0, max_new_tokens=128) ) output[0].response After some debugging, I was getting the following error: RuntimeError: MPS backend out of memory (MPS allocated: 22.64 GB, other allocations: 5.78 MB, max allowed: 22.64 GB). Tried to allocate 52.00 MB on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure). So is my machine not capable enough to adapter train Apple's Foundation Model? And if so, what's the recommended spec and could this be specified somewhere? Thanks!

Is there anywhere we can reference error codes? I'm getting this error: "The operation couldn’t be completed. (FoundationModels.LanguageModelSession.GenerationError error 4.)" and I have no idea of what it means or what to attempt to fix.

Download the Foundation Models Adaptor Training Toolkit Hi, after I clicked on the download button, I was redirected to this page https://developer.apple.com and did not download the toolkit.

Are there any details available on how Xcode 26 connects to third party model providers? For example, can Xcode only use OpenAI compatible API endpoints?

I'm seeing this error a lot in my console log of my iPhone 15 Pro (Apple Intelligence enabled): com.apple.modelcatalog.catalog sync: connection error during call: Error Domain=NSCocoaErrorDomain Code=4099 "The connection to service named com.apple.modelcatalog.catalog was invalidated: failed at lookup with error 159 - Sandbox restriction." UserInfo={NSDebugDescription=The connection to service named com.apple.modelcatalog.catalog was invalidated: failed at lookup with error 159 - Sandbox restriction.} reached max num connection attempts: 1 Are there entitlements / permissions I need to enable in Xcode that I forgot to do? Code example Here's how I'm initializing the language model session: private func setupLanguageModelSession() { if #available(iOS 26.0, *) { let instructions = """ my instructions """ do { languageModelSession = try LanguageModelSession(instructions: instructions) print("Foundation Models language model session initialized") } catch { print("Error creating language model session: \(error)") languageModelSession = nil } } else { print("Device does not support Foundation Models (requires iOS 26.0+)") languageModelSession = nil } }

Posting a follow up question after the WWDC 2025 Machine Learning AI & Frameworks Group Lab on June 12. In regards to the on-device API of any of the AI frameworks (foundation model, vision framework, ect.), is there a response condition or path where the API outsources it's input to ChatGPT if the user has allowed this like Siri does? Ignore this if it's a no: is this handled behind the scenes or by the developer?

Hi, I'm looking for the best way to use MLX models, particularly those I've fine-tuned, within a React Native application on iOS devices. Is there a recommended integration path or specific API for bridging MLX's capabilities to React Native for deployment on iPhones and iPads?

How do I test the new RecognizeDocumentRequest API. Reference: https://www.youtube.com/watch?v=H-GCNsXdKzM I am running Xcode Beta, however I only have one primary device that I cannot install beta software on. Please provide a strategy for testing. Will simulator work? The new capability is critical to my application, just what I need for structuring document scans and extraction. Thank you.

I couldn't find information about this in the documentation. Could someone clarify if this API is available and how to access it?

Hi everyone, I’m an AI engineer working on autonomous AI agents and exploring ways to integrate them into the Apple ecosystem, especially via Siri and Apple Intelligence. I was impressed by Apple’s integration of ChatGPT and its privacy-first design, but I’m curious to know: • Are there plans to support third-party LLMs? • Could Siri or Apple Intelligence call external AI agents or allow extensions to plug in alternative models for reasoning, scheduling, or proactive suggestions? I’m particularly interested in building event-driven, voice-triggered workflows where Apple Intelligence could act as a front-end for more complex autonomous systems (possibly local or cloud-based). This kind of extensibility would open up incredible opportunities for personalized, privacy-friendly use cases — while aligning with Apple’s system architecture. Is anything like this on the roadmap? Or is there a suggested way to prototype such integrations today? Thanks in advance for any thoughts or pointers!

Hi, i just wanna ask, Is it possible to run YOLOv3 on visionOS using the main camera to detect objects and show bounding boxes with labels in real-time? I’m wondering if camera access and custom models work for this, or if there’s a better way. Any tips?

I'm developing a tennis ball tracking feature using Vision Framework in Swift, specifically utilizing VNDetectedObjectObservation and VNTrackObjectRequest. Occasionally (but not always), I receive the following runtime error: Failed to perform SequenceRequest: Error Domain=com.apple.Vision Code=9 "Internal error: unexpected tracked object bounding box size" UserInfo={NSLocalizedDescription=Internal error: unexpected tracked object bounding box size} From my investigation, I suspect the issue arises when the bounding box from the initial observation (VNDetectedObjectObservation) is too small. However, Apple's documentation doesn't clearly define the minimum bounding box size that's considered valid by VNTrackObjectRequest. Could someone clarify: What is the minimum acceptable bounding box width and height (normalized) that Vision Framework's VNTrackObjectRequest expects? Is there any recommended practice or official guidance for bounding box size validation before creating a tracking request? This information would be extremely helpful to reliably avoid this internal error. Thank you!

Hi, I have been trying to integrate a CoreML model into Xcode. The model was made using tensorflow layers. I have included both the model info and a link to the app repository. I am mainly just really confused on why its not working. It seems to only be printing the result for case 1 (there are 4 cases labled, case 0, case 1, case 2, and case 3). If someone could help work me through this error that would be great! here is the link to the repository: https://github.com/ShivenKhurana1/Detect-to-Protect-App this file with the model code is called SecondView.swift and here is the model info: Input: conv2d_input-> image (color 224x224) Output: Identity -> MultiArray (Float32 1x4)

In an under-development MacOS & iOS app, I need to identify various measurements from OCR'ed text: length, weight, counts per inch, area, percentage. The unit type (e.g. UnitLength) needs to be identified as well as the measurement's unit (e.g. .inches) in order to convert the measurement to the app's internal standard (e.g. centimetres), the value of which is stored the relevant CoreData entity. The use of NLTagger and NLTokenizer is problematic because of the various representations of the measurements: e.g. "50g.", "50 g", "50 grams", "1 3/4 oz." Currently, I use a bespoke algorithm based on String contains and step-wise evaluation of characters, which is reasonably accurate but requires frequent updating as further representations are detected. I'm aware of the Python SpaCy model being capable of NER Measurement recognition, but am reluctant to incorporate a Python-based solution into a production app. (ref [https://developer.apple.com/forums/thread/30092]) My preference is for an open-source NER Measurement model that can be used as, or converted to, some form of a Swift compatible Machine Learning model. Does anyone know of such a model?

Hello, I posted an issue on the coremltools GitHub about my Core ML models not performing as well on iOS 17 vs iOS 16 but I'm posting it here just in case. TL;DR The same model on the same device/chip performs far slower (doesn't use the Neural Engine) on iOS 17 compared to iOS 16. Longer description The following screenshots show the performance of the same model (a PyTorch computer vision model) on an iPhone SE 3rd gen and iPhone 13 Pro (both use the A15 Bionic). iOS 16 - iPhone SE 3rd Gen (A15 Bioinc) iOS 16 uses the ANE and results in fast prediction, load and compilation times. iOS 17 - iPhone 13 Pro (A15 Bionic) iOS 17 doesn't seem to use the ANE, thus the prediction, load and compilation times are all slower. Code To Reproduce The following is my code I'm using to export my PyTorch vision model (using coremltools). I've used the same code for the past few months with sensational results on iOS 16. # Convert to Core ML using the Unified Conversion API coreml_model = ct.convert( model=traced_model, inputs=[image_input], outputs=[ct.TensorType(name="output")], classifier_config=ct.ClassifierConfig(class_names), convert_to="neuralnetwork", # compute_precision=ct.precision.FLOAT16, compute_units=ct.ComputeUnit.ALL ) System environment: Xcode version: 15.0 coremltools version: 7.0.0 OS (e.g. MacOS version or Linux type): Linux Ubuntu 20.04 (for exporting), macOS 13.6 (for testing on Xcode) Any other relevant version information (e.g. PyTorch or TensorFlow version): PyTorch 2.0 Additional context This happens across "neuralnetwork" and "mlprogram" type models, neither use the ANE on iOS 17 but both use the ANE on iOS 16 If anyone has a similar experience, I'd love to hear more. Otherwise, if I'm doing something wrong for the exporting of models for iOS 17+, please let me know. Thank you!

Not finding a lot on the Swift Assist technology announced at WWDC 2024. Does anyone know the latest status? Also, currently I use OpenAI's macOS app and its 'Work With...' functionality to assist with Xcode development, and this is okay, certainly saves copying code back and forth, but it seems like AI should be able to do a lot more to help with Xcode app development. I guess I'm looking at what people are doing with AI in Visual Studio, Cline, Cursor and other IDEs and tools like those and feel a bit left out working in Xcode. Please let me know if there are AI tools or techniques out there you use to help with your Xcode projects. Thanks in advance!

I have rewatched WWDC22 a few times , but still not getting full understanding how to get .mlmodel model file type from components . Example with banana ripeness is cool , but what need to be added to actually have output of .mlmodel , is somewhere full sample code for this type of modular project ? Code is from [https://developer.apple.com/videos/play/wwdc2022/10019) import CoreImage import CreateMLComponents struct ImageRegressor { static let trainingDataURL = URL(fileURLWithPath: "~/Desktop/bananas") static let parametersURL = URL(fileURLWithPath: "~/Desktop/parameters") static func train() async throws -> some Transformer<CIImage, Float> { let estimator = ImageFeaturePrint() .appending(LinearRegressor()) // File name example: banana-5.jpg let data = try AnnotatedFiles(labeledByNamesAt: trainingDataURL, separator: "-", index: 1, type: .image) .mapFeatures(ImageReader.read) .mapAnnotations({ Float($0)! }) let (training, validation) = data.randomSplit(by: 0.8) let transformer = try await estimator.fitted(to: training, validateOn: validation) try estimator.write(transformer, to: parametersURL) return transformer } } I have tried to run it in Mac OS command line type app, Swift-UI but most what I had as output was .pkg with "pipeline.json, parameters, optimizer.json, optimizer"

I have exported a Pytorch model into a CoreML mlpackage file and imported the model file into my iOS project. The model is a Music Source Separation model - running prediction on audio-spectrogram blocks and returning separated audio source spectrograms. Model produces correct results vs. desktop+GPU+Python but the inference on iPhone 15 Pro Max is really, really slow. Using Xcode model Performance tool I can see that the inference isn't automatically managed between compute units - all of it runs on CPU. The Performance tool notation hints all that ops should be supported by both the GPU and Neural Engine. One thing to note, that when initializing the model with MLModelConfiguration option .cpuAndGPU or .cpuAndNeuralEngine there is an error in Xcode console: `Error(s) occurred compiling MIL to BNNS graph: [CreateBnnsGraphProgramFromMIL]: Failed to determine convolution kernel at location at /private/var/containers/Bundle/Application/2E3C4AFF-1FA4-4C95-AAE4-ECEBC0FB0BF9/mymss.app/mymss.mlmodelc/model.mil:2453:12 @ CreateBnnsGraphProgramFromMIL` Before going back hammering the model in Python, are there any tips/strategies I could try in CoreMLTools export phase or in configuring the model for prediction on iOS? My export toolchain is currently Linux with CoreMLTools v8.1, export target iOS16.

Hello! I have a TrackNet model that I have converted to CoreML (.mlpackage) using coremltools, and the conversion process appears to go smoothly as I get the .mlpackage file I am looking for with the weights and model.mlmodel file in the folder. However, when I drag it into Xcode, it just shows up as 4 script tags instead of the model "interface" that is typically expected. I initially was concerned that my model was not compatible with CoreML, but upon logging the conversions, everything seems to be converted properly. I have some code that may be relevant in debugging this issue: How I use the model: model = BallTrackerNet() # this is the model architecture which will be referenced later device = self.device # cpu model.load_state_dict(torch.load("models/balltrackerbest.pt", map_location=device)) # balltrackerbest is the weights model = model.to(device) model.eval() Here is the BallTrackerNet() model itself import torch.nn as nn import torch class ConvBlock(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, pad=1, stride=1, bias=True): super().__init__() self.block = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=pad, bias=bias), nn.ReLU(), nn.BatchNorm2d(out_channels) ) def forward(self, x): return self.block(x) class BallTrackerNet(nn.Module): def __init__(self, out_channels=256): super().__init__() self.out_channels = out_channels self.conv1 = ConvBlock(in_channels=9, out_channels=64) self.conv2 = ConvBlock(in_channels=64, out_channels=64) self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv3 = ConvBlock(in_channels=64, out_channels=128) self.conv4 = ConvBlock(in_channels=128, out_channels=128) self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv5 = ConvBlock(in_channels=128, out_channels=256) self.conv6 = ConvBlock(in_channels=256, out_channels=256) self.conv7 = ConvBlock(in_channels=256, out_channels=256) self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv8 = ConvBlock(in_channels=256, out_channels=512) self.conv9 = ConvBlock(in_channels=512, out_channels=512) self.conv10 = ConvBlock(in_channels=512, out_channels=512) self.ups1 = nn.Upsample(scale_factor=2) self.conv11 = ConvBlock(in_channels=512, out_channels=256) self.conv12 = ConvBlock(in_channels=256, out_channels=256) self.conv13 = ConvBlock(in_channels=256, out_channels=256) self.ups2 = nn.Upsample(scale_factor=2) self.conv14 = ConvBlock(in_channels=256, out_channels=128) self.conv15 = ConvBlock(in_channels=128, out_channels=128) self.ups3 = nn.Upsample(scale_factor=2) self.conv16 = ConvBlock(in_channels=128, out_channels=64) self.conv17 = ConvBlock(in_channels=64, out_channels=64) self.conv18 = ConvBlock(in_channels=64, out_channels=self.out_channels) self.softmax = nn.Softmax(dim=1) self._init_weights() def forward(self, x, testing=False): batch_size = x.size(0) x = self.conv1(x) x = self.conv2(x) x = self.pool1(x) x = self.conv3(x) x = self.conv4(x) x = self.pool2(x) x = self.conv5(x) x = self.conv6(x) x = self.conv7(x) x = self.pool3(x) x = self.conv8(x) x = self.conv9(x) x = self.conv10(x) x = self.ups1(x) x = self.conv11(x) x = self.conv12(x) x = self.conv13(x) x = self.ups2(x) x = self.conv14(x) x = self.conv15(x) x = self.ups3(x) x = self.conv16(x) x = self.conv17(x) x = self.conv18(x) # x = self.softmax(x) out = x.reshape(batch_size, self.out_channels, -1) if testing: out = self.softmax(out) return out def _init_weights(self): for module in self.modules(): if isinstance(module, nn.Conv2d): nn.init.uniform_(module.weight, -0.05, 0.05) if module.bias is not None: nn.init.constant_(module.bias, 0) elif isinstance(module, nn.BatchNorm2d): nn.init.constant_(module.weight, 1) nn.init.constant_(module.bias, 0) I have been struggling with this conversion for almost 2 weeks now so any help, ideas or pointers would be greatly appreciated! Thanks! Michael

import coremltools as ct from coremltools.models.neural_network import quantization_utils # load full precision model model_fp32 = ct.models.MLModel(modelPath) model_fp16 = quantization_utils.quantize_weights(model_fp32, nbits=16) model_fp16.save("reduced-model.mlmodel") I'm testing it with the model from one of Apple's source codes(GameBoardDetector), and it works fine, reduces the model size by half. But there are several problems with my model(trained on CreateML app using Full Network): Quantizing to float 16 does not work(new file gets created with reduced only 0.1mb). Quantizing to below 16 values cause errors, and no file gets created. Here are additional metadata and precisions of models. Working model's additional metadata and precision: Mine's additional metadata and precision: