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

The WWDC25: Explore large language models on Apple silicon with MLX video talks about using your own data to fine-tune a large language model. But the video doesn't explain what kind of data can be used. The video just shows the command to use and how to point to the data folder. Can I use PDFs, Word documents, Markdown files to train the model? Are there any code examples on GitHub that demonstrate how to do this?

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 } }

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.

I have a fairly basic prompt I've created that parses a list of locations out of a string. I've then created a tool, which for these locations, finds their latitude/longitude on a map and populates that in the response. However, I cannot get the language model session to see/use my tool. I have code like this passing the tool to my prompt: class Parser { func populate(locations: String, latitude: Double, longitude: Double) async { let findLatLonTool = FindLatLonTool(latitude: latitude, longitude: longitude) let session = LanguageModelSession(tools: [findLatLonTool]) { """ A prompt that populates a model with a list of locations. """ """ Use the findLatLon tool to populate the latitude and longitude for the name of each location. """ } let stream = session.streamResponse(to: "Parse these locations: \(locations)", generating: ParsedLocations.self) let locationsModel = LocationsModels(); do { for try await partialParsedLocations in stream { locationsModel.parsedLocations = partialParsedLocations.content } } catch { print("Error parsing") } } } And then the tool that looks something like this: import Foundation import FoundationModels import MapKit struct FindLatLonTool: Tool { typealias Output = GeneratedContent let name = "findLatLon" let description = "Find the latitude / longitude of a location for a place name." let latitude: Double let longitude: Double @Generable struct Arguments { @Guide(description: "This is the location name to look up.") let locationName: String } func call(arguments: Arguments) async throws -> GeneratedContent { let request = MKLocalSearch.Request() request.naturalLanguageQuery = arguments.locationName request.region = MKCoordinateRegion( center: CLLocationCoordinate2D(latitude: latitude, longitude: longitude), latitudinalMeters: 1_000_000, longitudinalMeters: 1_000_000 ) let search = MKLocalSearch(request: request) let coordinate = try await search.start().mapItems.first?.location.coordinate if let coordinate = coordinate { return GeneratedContent( LatLonModel(latitude: coordinate.latitude, longitude: coordinate.longitude) ) } return GeneratedContent("Location was not found - no latitude / longitude is available.") } } But trying a bunch of different prompts has not triggered the tool - instead, what appear to be totally random locations are filled in my resulting model and at no point does a breakpoint hit my tool code. Has anybody successfully gotten a tool to be called?

We are developing Apple AI for overseas markets and adapting it for iPhone 17 and later models. When the system language and Siri language do not match—such as the system being in English while Siri is in Chinese—it may result in Apple AI being unusable. So, I would like to ask, how can this issue be resolved, and are there other reasons that might cause it to be unusable within the app?

No matter what, the LanguageModelSession always returns very lengthy / verbose responses. I set the maximumResponseTokens option to various small numbers but it doesn't appear to have any effect. I've even used this instructions format to keep responses between 3-8 words but it returns multiple paragraphs. Is there a way to manage LLM response length? Thanks.

When using Foundation Models, is it possible to ask the model to produce output in a specific language, apart from giving an instruction like "Provide answers in ." ? (I tried that and it kind of worked, but it seems fragile.) I haven't noticed an API to do so and have a use-case where the output should be in a user-selectable language that is not the current system language.

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!

Hello! I'm following the Foundation Models adapter training guide (https://developer.apple.com/apple-intelligence/foundation-models-adapter/) on my NVIDIA DGX Spark box. I'm able to train on my own data but the example notebook fails when I try to export the artifact as an fmadapter. I get the following error for the code block I'm trying to run. I haven't touched any of the code in the export folder. I tried exporting it on my Mac too and got the same error as well (given below). Would appreciate some more clarity around this. Thank you. Code Block: from export.export_fmadapter import Metadata, export_fmadapter metadata = Metadata( author="3P developer", description="An adapter that writes play scripts.", ) export_fmadapter( output_dir="./", adapter_name="myPlaywritingAdapter", metadata=metadata, checkpoint="adapter-final.pt", draft_checkpoint="draft-model-final.pt", ) Error: --------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) Cell In[10], line 1 ----> 1 from export.export_fmadapter import Metadata, export_fmadapter 3 metadata = Metadata( 4 author="3P developer", 5 description="An adapter that writes play scripts.", 6 ) 8 export_fmadapter( 9 output_dir="./", 10 adapter_name="myPlaywritingAdapter", (...) 13 draft_checkpoint="draft-model-final.pt", 14 ) File /workspace/export/export_fmadapter.py:11 8 from typing import Any 10 from .constants import BASE_SIGNATURE, MIL_PATH ---> 11 from .export_utils import AdapterConverter, AdapterSpec, DraftModelConverter, camelize 13 logger = logging.getLogger(__name__) 16 class MetadataKeys(enum.StrEnum): File /workspace/export/export_utils.py:15 13 import torch 14 import yaml ---> 15 from coremltools.libmilstoragepython import _BlobStorageWriter as BlobWriter 16 from coremltools.models.neural_network.quantization_utils import _get_kmeans_lookup_table_and_weight 17 from coremltools.optimize._utils import LutParams ModuleNotFoundError: No module named 'coremltools.libmilstoragepython'

Hello folks! Taking a look at https://developer.apple.com/documentation/foundationmodels it’s not clear how to use another models there. Do anyone knows if it’s possible use one trained model from outside (imported) here in foundation models framework? Thanks!

Also submitted as feedback (ID: FB20612561). Tensorflow-metal fails on tensorflow versions above 2.18.1, but works fine on tensorflow 2.18.1 In a new python 3.12 virtual environment: pip install tensorflow pip install tensor flow-metal python -c "import tensorflow as tf" Prints error: Traceback (most recent call last): File "", line 1, in File "/Users//pt/venv/lib/python3.12/site-packages/tensorflow/init.py", line 438, in _ll.load_library(_plugin_dir) File "/Users//pt/venv/lib/python3.12/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users//pt/venv/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Library not loaded: @rpath/_pywrap_tensorflow_internal.so Referenced from: <8B62586B-B082-3113-93AB-FD766A9960AE> /Users//pt/venv/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib Reason: tried: '/Users//pt/venv/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/Users//pt/venv/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/opt/homebrew/lib/_pywrap_tensorflow_internal.so' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/opt/homebrew/lib/_pywrap_tensorflow_internal.so' (no such file)

Has Apple made any commitment to versioning the Foundation Models on device? What if you build a feature that works great on 26.0 but they change the model or guardrails in 26.1 and it breaks your feature, is your only recourse filing Feedback or pulling the feature from the app? Will there be a way to specify a model version like in all of the server based LLM provider APIs? If not, sounds risky to build on.

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 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!

I'm attempting to run a basic Foundation Model prototype in Xcode 26, but I'm getting the error below, using the iPhone 16 simulator with iOS 26. Should these models be working yet? Do I need to be running macOS 26 for these to work? (I hope that's not it) Error: Passing along Model Catalog error: Error Domain=com.apple.UnifiedAssetFramework Code=5000 "There are no underlying assets (neither atomic instance nor asset roots) for consistency token for asset set com.apple.MobileAsset.UAF.FM.Overrides" UserInfo={NSLocalizedFailureReason=There are no underlying assets (neither atomic instance nor asset roots) for consistency token for asset set com.apple.MobileAsset.UAF.FM.Overrides} in response to ExecuteRequest Playground to reproduce: #Playground { let session = LanguageModelSession() do { let response = try await session.respond(to: "What's happening?") } catch { let error = error } }