In the name of God, please allow initializing GeneratedContent from an array of key-value pairs. It’s literally the same thing KeyValuePairs uses internally, but it would let us initialize structure-like GeneratedContent from dynamic data without resorting to unsafeBitCast hacks.

I'm testing Foundation Model on my iPad Pro (5th gen) iOS 26. Up until late this morning, I can no longer load the SystemLanguageModel.default. I'm not doing anything interesting, something as basic as this is only going to unavailable, specifically I get unavailable reason: modelNotReady. let model = SystemLanguageModel.default ... switch model.availability { case .available: print("LM available") case .unavailable(let reason): print("unavailable reason: ", String(describing: reason)) } I also ran the FoundationModelsTripPlanner app, same thing. It was working yesterday, I have not modified that project either. Why is the Model not ready? How do I fix this? Yes, I tried restarting both my laptop and iPad, no luck.

Hi everyone, I am using Xcode 16.4 in MacOS Sequoia 15.5 with Apple Intelligence turned on. The following code gives the error message in the title: import NaturalLanguage @available(iOS 18.0, *) func testSystemModel() { let model = SystemLanguageModel.default print(model) } What am I missing?

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.

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 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 } }

Hi, I just upgraded my macOS with beta 2. After upgrade, the SwiftTranscriptionSampleApp it's stopped working, in Xcode console I read: "The connection to service named com.apple.modelcatalog.catalog was invalidated: failed at lookup with error 159 - Sandbox restriction." Note: in beta 1 worked fine

I am calling into an app extension from a Safari Web Extension (sendNativeMessage, which in turn results in a call to NSExtensionRequestHandling’s beginRequest). My Safari extension aims to make use of the new foundation models for some of the features it provides. In my testing, I hit the rate limit by sending 4 requests, waiting 30 seconds between each. This makes the FoundationModels framework (which would otherwise serve my use case perfectly well) unusable in this context, because the model is called in response to user input, and this rate of user input is perfectly plausible in a real world scenario. The error thrown as a result of the rate limit is “Safety guardrail was triggered after consecutive failures during streaming.", but looking at the system logs in Console.app shows the rate limit as the real culprit. My suggestions: Please introduce sensible rate limits for app extensions, through an entitlement if need be. If it is rate limited to 1 request per every couple of seconds, that would already fix the issue for me. Please document the rate limit. Please make the thrown error reflect that it is the result of a rate limit and not a generic guardrail violation. IMPORTANT: please indicate in the thrown error when it is safe to try again. Filed a feedback here: FB18332004

When I try to run visionOS 26 beta 2 on my device the app crashes on Launch: dyld[904]: Symbol not found: _$s16FoundationModels10TranscriptV7entriesACSayAC5EntryOG_tcfC Referenced from: <A71932DD-53EB-39E2-9733-32E9D961D186> /private/var/containers/Bundle/Application/53866099-99B1-4BBD-8C94-CD022646EB5D/VisionPets.app/VisionPets.debug.dylib Expected in: <F68A7984-6B48-3958-A48D-E9F541868C62> /System/Library/Frameworks/FoundationModels.framework/FoundationModels Symbol not found: _$s16FoundationModels10TranscriptV7entriesACSayAC5EntryOG_tcfC Referenced from: <A71932DD-53EB-39E2-9733-32E9D961D186> /private/var/containers/Bundle/Application/53866099-99B1-4BBD-8C94-CD022646EB5D/VisionPets.app/VisionPets.debug.dylib Expected in: <F68A7984-6B48-3958-A48D-E9F541868C62> /System/Library/Frameworks/FoundationModels.framework/FoundationModels dyld config: DYLD_LIBRARY_PATH=/usr/lib/system/introspection DYLD_INSERT_LIBRARIES=/usr/lib/libLogRedirect.dylib:/usr/lib/libBacktraceRecording.dylib:/usr/lib/libMainThreadChecker.dylib:/usr/lib/libViewDebuggerSupport.dylib:/System/Library/PrivateFrameworks/GPUToolsCapture.framework/GPUToolsCapture Symbol not found: _$s16FoundationModels10TranscriptV7entriesACSayAC5EntryOG_tcfC Referenced from: <A71932DD-53EB-39E2-9733-32E9D961D186> /private/var/containers/Bundle/Application/53866099-99B1-4BBD-8C94-CD022646EB5D/VisionPets.app/VisionPets.debug.dylib Expected in: <F68A7984-6B48-3958-A48D-E9F541868C62> /System/Library/Frameworks/FoundationModels.framework/FoundationModels dyld config: DYLD_LIBRARY_PATH=/usr/lib/system/introspection DYLD_INSERT_LIBRARIES=/usr/lib/libLogRedirect.dylib:/usr/lib/libBacktraceRecording.dylib:/usr/lib/libMainThreadChecker.dylib:/usr/lib/libViewDebuggerSupport.dylib:/System/Library/PrivateFrameworks/GPUToolsCapture.framework/GPUToolsCapture Message from debugger: Terminated due to signal 6

It seems like there was an undocumented change that made Transcript.init(entries: [Transcript.Entry] initializer private, which broke my application, which relies on (manual) reconstruction of Transcript entries. Worked fine on beta 1, on beta 2 there's this error dyld[72381]: Symbol not found: _$s16FoundationModels10TranscriptV7entriesACSayAC5EntryOG_tcfC Referenced from: <44342398-591C-3850-9889-87C9458E1440> /Users/mika/experiments/apple-on-device-ai/fm Expected in: <66A793F6-CB22-3D1D-A560-D1BD5B109B0D> /System/Library/Frameworks/FoundationModels.framework/Versions/A/FoundationModels Is this a part of an API transition, if so - Apple, please update your documentation

I was able to open a new project and play around with the Foundation Model, but when I dropped this class in a production app (with a lot of files) I'm running into Safety Guardrail errors for this very small prompt. Specifically it's "Safety guardrail was triggered after consecutive failures during streaming." Does it have something to do with the size of the app? I don't know what else to try to get it to work? import FoundationModels import Playgrounds @available(iOS 26.0, *) #Playground { Task { do { let session = LanguageModelSession() let prompt = "Write a short story about a talking cat." let response = try await session.respond(to: prompt) print(response) } catch { print("Error: \(error)") } } }

Hi, I just upgraded to macOS Tahoe Beta 2 and now I'm getting this error when I try to initialize my Foundation Models' session: Error Resource (Local Sanitizer Asset) unavailable error. import FoundationModels #Playground { let session = LanguageModelSession() do { let result = try await session.respond(to: "Tell me 3 colors") print(result.content) } catch { print("Error", error) } } I couldn't find any resource guiding me on how to solve this. Any help/workaround? Thank you!

I am using a contact tool to help get contact from my address book. but the model ins't invoking my tool call method. Even tried with a simple tool the outcome is the same my simple tool is not being invoked.

For example: I have a list of to-dos, each with a unique id (a GUID). I want to feed them to the LLM model and have the model rewrite the items so they start with an action verb. I'd like to get them back and identify which rewritten item corresponds to which original item. I obviously can't compare the text, as it has changed. I've tried passing the original GUIDs in with each to-do, but the extra GUID characters pollutes the input and confuses the model. I've tried numbering them in order and adding an originalSortOrder field to my generable type, but it doesn't work reliably. Any suggestions? I could do them one at a time, but I also have a use case where I'm asking for them to be organized in sections, and while I've instructed the model not to rename anything, it still happens. It's just all very nondeterministic.

I am using Foundation Models for the first time and no response is being provided to me. Code import Playgrounds import FoundationModels #Playground { let session = LanguageModelSession() let result = try await session.respond(to: "List all the states in the USA") print(result.content) } Canvas Output What I did New file Code Canvas refreshes but nothing happens Am I missing a step or setup here? Please help. Something so basic is not working I do not know what to do. Running 40GPU, 16CPU MacBook Pro.. IOS26/Xcodebeta2/Tahoe allocated 8CPU, 48GB memory in Parallels VM. Settings for Playgrounds in Xcode Thank you for your help in advance.

Hello Team, I'm currently working on a proof of concept using Apple's Foundation Model for a RAG-based chat system on my MacBook Pro with the M1 Max chip. Environment details: macOS: 26.0 Beta Xcode: 26.0 beta 2 (17A5241o) Target platform: iPad (as the iPhone simulator does not support Foundation models) While testing, even with very small input prompts to the LLM, I intermittently encounter the following error: InferenceError::inference-Failed::Failed to run inference: Context length of 4096 was exceeded during singleExtend. Has anyone else experienced this issue? Are there known limitations or workarounds for context length handling in this setup? Any insights would be appreciated. Thank you!

We are really excited to have introduced the Foundation Models framework in WWDC25. When using the framework, you might have feedback about how it can better fit your use cases. Starting in macOS/iOS 26 Beta 4, the best way to provide feedback is to use #Playground in Xcode. To do so: In Xcode, create a playground using #Playground. Fore more information, see Running code snippets using the playground macro. Reproduce the issue by setting up a session and generating a response with your prompt. In the canvas on the right, click the thumbs-up icon to the right of the response. Follow the instructions on the pop-up window and submit your feedback by clicking Share with Apple. Another way to provide your feedback is to file a feedback report with relevant details. Specific to the Foundation Models framework, it’s super important to add the following information in your report: Language model feedback This feedback contains the session transcript, including the instructions, the prompts, the responses, etc. Without that, we can’t reason the model’s behavior, and hence can hardly take any action. Use logFeedbackAttachment(sentiment:issues:desiredOutput: ) to retrieve the feedback data of your current model session, as shown in the usage example, write the data into a file, and then attach the file to your feedback report. If you believe what you’d report is related to the system configuration, please capture a sysdiagnose and attach it to your feedback report as well. The framework is still new. Your actionable feedback helps us evolve the framework quickly, and we appreciate that. Thanks, The Foundation Models framework team

With respond() methods, the foundation model works well enough. With streamResponse() methods, the responses are very repetitive, verbose, and messy. My app with foundation model uses more than 500 MB memory on an iPad Pro when running from Xcode. Devices supporting Apple Intelligence have at least 8GB memory. Should Apple use a bigger model (using 3 ~ 4 GB memory) for better stream responses?

When I initialize a session with an existing transcript using this initializer: public convenience init(model: SystemLanguageModel = .default, guardrails: LanguageModelSession.Guardrails = .default, tools: [any Tool] = [], transcript: Transcript) The tools get ignored. I noticed that when doing that, the model never use the tools. When inspecting the transcript, I can see that the instruction entry does not have any tools available to it. I tried this for both transcripts that already include an instruction entry and ones that don't - both yielding the same result.. Is this the intended behavior / am I missing something here?

Hey everyone I'm Manish Mehta, field CTO at Centific. I recently read Apple's white paper, The Illusion of Thinking and it got me thinking about the current state of AI reasoning. Who here has read it? The paper highlights how LLMs often rely on pattern recognition rather than genuine understanding. When faced with complex tasks, their performance can degrade significantly. I was just thinking that to move beyond this problem, we need to explore approaches that combines Deeper Reasoning Architectures for true cognitive capability with Deep Human Partnership to guide AI toward better judgment and understanding. The first part means fundamentally rewiring AI to reason. This involves advancing deeper architectures like World Models, which can build internal simulations to understand real-world scenarios , and Neurosymbolic systems, which combines neural networks with symbolic reasoning for deeper self-verification. Additionally, we need to look at deep human partnership and scalable oversight. An AI cannot learn certain things from data alone, it lacks the real-world judgment an AI will never have. Among other things, deep domain expert human partners are needed to instill this wisdom , validate the AI's entire reasoning process , build its ethical guardrails , and act as skilled adversaries to find hidden flaws before they can cause harm. What do you all think? Is this focus on a deeper partnership between advanced AI reasoning and deep human judgment the right path forward? Agree? Disagree? Thanks