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.
Foundation Models
RSS for tagDiscuss the Foundation Models framework which provides access to Apple’s on-device large language model that powers Apple Intelligence to help you perform intelligent tasks specific to your app.
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I'm new to Swift and was hoping the Playground would support loading adaptors. When I tried, I got a permissions error - thinking it's because it's not in the project and Playgrounds don't like going outside the project?
A tutorial and some sample code would be helpful.
Also some benchmarks on how long it's expected to take. Selfishly I'm on an M2 Mac Mini.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
Hey,
When generating responses with structured output and non-streaming API, it sometimes takes 3s, sometimes 10-20s. I am firing that request subsequently while testing the app.
Is this by design, or any place I can learn more about what contributes to such variation?
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
@Generable
enum Breakfast {
case waffles
case pancakes
case bagels
case eggs
}
do {
let session = LanguageModelSession()
let userInput = "I want something sweet."
let prompt = "Pick the ideal breakfast for request: (userInput)"
let response = try await session.respond(to: prompt,generating: Breakfast.self)
print(response.content)
} catch let error {
print(error)
}
i want to test the @Generable demo but get error with below:decodingFailure(FoundationModels.LanguageModelSession.GenerationError.Context(debugDescription: "Failed to convert text into into GeneratedContent\nText: waffles", underlyingErrors: [Swift.DecodingError.dataCorrupted(Swift.DecodingError.Context(codingPath: [], debugDescription: "The given data was not valid JSON.", underlyingError: Optional(Error Domain=NSCocoaErrorDomain Code=3840 "Unexpected character 'w' around line 1, column 1." UserInfo={NSJSONSerializationErrorIndex=0, NSDebugDescription=Unexpected character 'w' around line 1, column 1.})))]))
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
Hey
Tried using a few regular expressions and all fail with an error:
Unhandled error streaming response: A generation guide with an unsupported pattern was used.
Is there are a list of supported features? I don't see it in docs, and it takes RegExp.
Anything with e.g. [A-Z] fails.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
In the play ground I'm trying to bias my LanguageModel to use a tool I registered, but I don't see it actually calling the tool. I'm following the developer video on landmarks itinerary generation tutorial almost verbatim. Is this a prompt engineering thing I'm missing? Or is it possible that I'm injecting my tool wrong?
Apologies if this is obvious to everyone but me... I'm using the Tahoe AI foundation models. When I get an error, I'm trying to handle it properly.
I see the errors described here: https://developer.apple.com/documentation/foundationmodels/languagemodelsession/generationerror/context, as well as in the headers. But all I can figure out how to see is error.localizedDescription which doesn't give me much to go on.
For example, an error's description is:
The operation couldn’t be completed. (FoundationModels.LanguageModelSession.GenerationError error 2.
That doesn't give me much to go on. How do I get the actual error number/enum value out of this, short of parsing that text to look for the int at the end?
This one is:
case guardrailViolation(LanguageModelSession.GenerationError.Context)
So I'd like to know how to get from the catch for session.respond to something I can act on. I feel like it's there, but I'm missing it.
Thanks!
Does the Foundation Model provide Objective-C compatible APIs?
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
According to the Tool documentation, the arguments to the tool are specified as a static struct type T, which is given to tool.call(argument: T) However, if the arguments are not known until runtime, is it possible to still create a Tool object with the proper parameters? Let's say a JSON-style dictionary is passed into the Tool init function to specify T, is this achievable?
This is my code:
witch SystemLanguageModel.default.availability {
case .available:
ContentView()
.popover(isPresented: $showSettings) {
SettingsView().presentationCompactAdaptation(.popover)
}
case .unavailable(.modelNotReady):
ContentUnavailableView("Apple Intelligence is unavailable",
systemImage: "apple.intelligence.badge.xmark",
description: Text("Please come back later."))
case .unavailable(.appleIntelligenceNotEnabled):
ContentUnavailableView("Apple Intelligence is unavailable",
systemImage: "apple.intelligence.badge.xmark",
description: Text("Please turn on Apple Intelligence."))
case .unavailable(.deviceNotEligible):
ContentUnavailableView("Apple Intelligence is unavailable",
systemImage: "apple.intelligence.badge.xmark",
description: Text("This device is not eligible for Apple Intelligence."))
case .unavailable:
ContentUnavailableView("Apple Intelligence is unavailable",
systemImage: "apple.intelligence.badge.xmark")
}
When I switch off Apple Intelligence, I expected "Please turn on Apple Intelligence.", but instead I get "Please come back later."
This seems to be wrong error?
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
I get the following dyld error on an iPad Pro with Xcode 26 beta 4:
Symbol not found: _$s16FoundationModels20LanguageModelSessionC7prewarm12promptPrefixyAA6PromptVSg_tF
Any advice?
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
I've been successfully integrating the Foundation Models framework into my healthcare app using structured generation with @Generable schemas. While my initial testing (20-30 iterations) shows promising results, I need to validate consistency and reliability at scale before production deployment.
Question
Is there a recommended approach for automated, large-scale testing of Foundation Models responses?
Specifically, I'm looking to:
Automate 1000+ test iterations with consistent prompts and structured schemas
Measure response consistency across identical inputs
Validate structured output reliability (proper schema adherence, no generation failures)
Collect performance metrics (TTFT, TPS) for optimization
Specific Questions
Framework Limitations: Are there any undocumented rate limits or thermal throttling considerations for rapid session creation/destruction?
Performance Tools: Can Xcode's Foundation Models Instrument be used programmatically, or only through Instruments UI?
Automation Integration: Any recommendations for integrating with testing frameworks?
Session Reuse: Is it better to reuse a single LanguageModelSession or create fresh sessions for each test iteration?
Use Case Context
My wellness app provides medically safe activity recommendations based on user health profiles. The Foundation Models framework processes health context and generates structured recommendations for exercises, nutrition, and lifestyle activities. Given the safety implications of providing health-related guidance, I need rigorous validation to ensure the model consistently produces appropriate, well-formed recommendations across diverse user scenarios and health conditions.
Has anyone in the community built similar large-scale testing infrastructure for Foundation Models? Any insights on best practices or potential pitfalls would be greatly appreciated.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
I am trying to create a slightly different version of the content tagging code in the documentation:
https://developer.apple.com/documentation/foundationmodels/systemlanguagemodel/usecase/contenttagging
In the playground I am getting an "Inference Provider crashed with 2:5" error.
I have no idea what that means or how to address the error. Any assistance would be appreciated.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
Problem:
We trained a LoRA adapter for Apple's FoundationModels framework using their TAMM (Training Adapter for Model Modification)
toolkit v0.2.0 on macOS 26 beta 4. The adapter trains successfully but fails to load with: "Adapter is not compatible with the
current system base model."
TAMM 2.0 contains export/constants.py with: BASE_SIGNATURE = "9799725ff8e851184037110b422d891ad3b92ec1"
Findings:
Adapter Export Process:
In export_fmadapter.py
def write_metadata(...):
self_dict[MetadataKeys.BASE_SIGNATURE] = BASE_SIGNATURE # Hardcoded value
The Compatibility Check:
- When loading an adapter, Apple's system compares the adapter's baseModelSignature with the current system model
- If they don't match: compatibleAdapterNotFound error
- The error doesn't reveal the expected signature
Questions:
- How is BASE_SIGNATURE derived from the base model?
- Is it SHA-1 of base-model.pt or some other computation?
- Can we compute the correct signature for beta 4?
- Or do we need Apple to release TAMM v0.3.0 with updated signature?
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
Tags:
Core ML
Create ML
tensorflow-metal
Apple Intelligence
On macOS Tahoe26.0, iOS 26.0 (23A5287g) not emulator, Xcode 26.0 beta 3 (17A5276g)
Follow this tutorial Testing your asset packs locally The start the test server command I use this command line to start the test server:xcrun ba-serve --host 192.168.0.109 test.aar The terminal showThe content displayed on the terminal is: Loading asset packs…
Loading the asset pack at “test.aar”…
Listening on port 63125…… Choose an identity in the panel to continue. Listening on port 63125…
running the project, Xcode reports an error:Download failed: Could not connect to the server. I use iPhone safari visit this website: https://192.168.0.109:63125, on the page display "Hello, world!"
There are too few error messages in both of the above questions. I have no idea what the specific reasons are.I hope someone can offer some guidance. Best Regards.
{
"assetPackID": "testVideoAssetPack",
"downloadPolicy": {
"prefetch": {
"installationEventTypes": ["firstInstallation", "subsequentUpdate"]
}
},
"fileSelectors": [
{
"file": "video/test.mp4"
}
],
"platforms": [
"iOS"
]
}
this is my Manifest.json
Hi,
I’m developing an app targeting iOS 26, using the new FoundationModels framework to perform on-device LLM inference. I’m currently testing memory usage.
Does the memory used by FoundationModels—including model weights, KV cache, and any inference-related buffers—count toward my app’s Jetsam memory limit, or is any of it managed separately by the system?
I may need to run two concurrent inferences, each with a 4096-token context window. Is this explicitly supported or allowed by FoundationModels on iOS 26? Would this significantly increase the risk of memory-based termination?
Thanks in advance for any clarification.
Thanks.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
I'm working on localizing my prompts to support multiple languages, and in some cases my prompts has String interpolated Generable objects. for example:
"Given the following workout routine: \(routine), suggest one additional exercise to complement it."
In the Strings dictionary, I'm only able to select String, Int or Double parameters using %@ and %lld.
Has anyone found a way to accomplish this?
Encountered a few times when the answer get "stuck" (I am now at beta 6).
This is an example.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
Was just wondering why the foundation model documentation is no longer available, thanks!
https://developer.apple.com/documentation/FoundationModels
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
I have integrated Apple’s Foundation Model into my iOS application. As known, Foundation Model is only supported starting from iOS 26 on compatible devices. To maintain compatibility with older iOS versions, I wrapped the API calls with the condition if #available(iOS 26, *).
The application works normally on an iPad running iOS 18 and on a Mac running macOS 26. However, when running the same build on a MacBook Air M1 (macOS 15) through iPad app compatibility, the app crashes immediately upon launch.
The main issue is that I cannot debug directly on macOS 15, since the app can only be built on macOS 26 with Xcode beta. I then have to distribute it via TestFlight and download it on the MacBook Air M1 for testing. This makes identifying the detailed cause of the crash very difficult and time-consuming.
Nevertheless, I have confirmed that the crash is caused by the Foundation Model APIs.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models