Using Tensorflow for Silicon gives inaccurate results when compared to Google Colab GPU (9-15% differences). Here are my install versions for 4 anaconda env's. I understand the Floating point precision can be an issue, batch size, activation functions but how do you rectify this issue for the past 3 years? 1.) Version TF: 2.12.0, Python 3.10.13, tensorflow-deps: 2.9.0, tensorflow-metal: 1.2.0, h5py: 3.6.0, keras: 2.12.0 2.) Version TF: 2.19.0, Python 3.11.0, tensorflow-metal: 1.2.0, h5py: 3.13.0, keras: 3.9.2, jax: 0.6.0, jax-metal: 0.1.1,jaxlib: 0.6.0, ml_dtypes: 0.5.1 3.) python: 3.10.13,tensorflow: 2.19.0,tensorflow-metal: 1.2.0, h5py: 3.13.0, keras: 3.9.2, ml_dtypes: 0.5.1 4.) Version TF: 2.16.2, tensorflow-deps:2.9.0,Python: 3.10.16, tensorflow-macos 2.16.2, tensorflow-metal: 1.2.0, h5py:3.13.0, keras: 3.9.2, ml_dtypes: 0.3.2 Install of Each ENV with common example: Create ENV: conda create --name TF_Env_V2 --no-default-packages start env: source TF_Env_Name ENV_1.) conda install -c apple tensorflow-deps , conda install tensorflow,pip install tensorflow-metal,conda install ipykernel ENV_2.) conda install pip python==3.11, pip install tensorflow,pip install tensorflow-metal,conda install ipykernel ENV_3) conda install pip python 3.10.13,pip install tensorflow, pip install tensorflow-metal,conda install ipykernel ENV_4) conda install -c apple tensorflow-deps, pip install tensorflow-macos, pip install tensor-metal, conda install ipykernel Example used on all 4 env: import tensorflow as tf cifar = tf.keras.datasets.cifar100 (x_train, y_train), (x_test, y_test) = cifar.load_data() model = tf.keras.applications.ResNet50( include_top=True, weights=None, input_shape=(32, 32, 3), classes=100,) loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False) model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"]) model.fit(x_train, y_train, epochs=5, batch_size=64)

Hi all, I'm tuning my app prediction speed with Core ML model. I watched and tried the methods in video: Improve Core ML integration with async prediction and Optimize your Core ML usage. I also use instruments to look what's the bottleneck that my prediction speed cannot be faster. Below is the instruments result with my app. its prediction duration is 10.29ms And below is performance report shows the average speed of prediction is 5.55ms, that is about half time of my app prediction! Below is part of my instruments records. I think the prediction should be considered quite frequent. Could it be faster? How to be the same prediction speed as performance report? The prediction speed on macbook Pro M2 is nearly the same as macbook Air M1!

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.

In working with Apple's foundation models, we often want to provide as much context as possible. However, since the model has a context size limit of 4096 tokens, is there a way to estimate the number of tokens beforehand?

Hello, I am developing an app for the Swift Student challenge; however, I keep encountering an error when using ClassifyImageRequest from the Vision framework in Xcode: VTEST: error: perform(_:): inside 'for await result in resultStream' error: internalError("Error Domain=NSOSStatusErrorDomain Code=-1 \"Failed to create espresso context.\" UserInfo={NSLocalizedDescription=Failed to create espresso context.}") It works perfectly when testing it on a physical device, and I saw on another thread that ClassifyImageRequest doesn't work on simulators. Will this cause problems with my submission to the challenge? Thanks

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

Hello, Are there any plans to compile a python 3.13 version of tensorflow-metal? Just got my new Mac mini and the automatically installed version of python installed by brew is python 3.13 and while if I was in a hurry, I could manage to get python 3.12 installed and use the corresponding tensorflow-metal version but I'm not in a hurry. Many thanks, Alan

I am watching a few WWDC sessions on Foundation Model and its usage and it looks pretty cool. I was wondering if it is possible to perform RAG on the user documents on the devices and entuallly on iCloud... Let's say I have a lot of pages documents about me and I want the Foundation model to access those information on the documents to answer questions about me that can be retrieved from the documents. How can this be done ? Thanks

Hello, I am testing the sample project provided here: Bringing advanced speech-to-text capabilities to your app. On both macOS 26.0 beta and iOS 26.0 beta, the app crashes immediately on launch with a dyld "Symbol not found" error related to FoundationModels.framework. It feels like this may be related to testing primarily on newer Apple Silicon devices, as I am seeing consistent crashes on an Intel MacBook and on an older iPhone device. I would appreciate any insight, confirmation, or guidance on whether this is a known limitation or if there is a workaround. Is it planned to be resolved soon? Environment macOS: Device: MacBook Pro (Intel) Processor: 2 GHz Quad-Core Intel Core i5 Graphics: Intel Iris Plus Graphics 1536 MB Memory: 16 GB 3733 MHz LPDDR4X OS: macOS Tahoe Version 26.0 Beta (25A5338b) iOS: Device: iPhone 11 Model Number: MHDD3HN/A OS: iOS 26.0 Xcode: Version: 26.0 beta 3 (17A5276g) Crash (macOS) Abort signal received. Excerpt from crash dump: dyld`__abort_with_payload: 0x7ff80e3ad4a0 &lt;+0&gt;: movl $0x2000209, %eax 0x7ff80e3ad4a5 &lt;+5&gt;: movq %rcx, %r10 0x7ff80e3ad4a8 &lt;+8&gt;: syscall -&gt; 0x7ff80e3ad4aa &lt;+10&gt;: jae 0x7ff80e3ad4b4 Console: dyld[9819]: Symbol not found: _$s16FoundationModels20LanguageModelSessionC5model10guardrails5tools12instructionsAcA06SystemcD0C_AC10GuardrailsVSayAA4Tool_pGAA12InstructionsVSgtcfC Referenced from: /Users/userx/Library/Developer/Xcode/DerivedData/SwiftTranscriptionSampleApp-*/Build/Products/Debug/SwiftTranscriptionSampleApp.app/Contents/MacOS/SwiftTranscriptionSampleApp.debug.dylib Expected in: /System/Library/Frameworks/FoundationModels.framework/Versions/A/FoundationModels Crash (iOS) Abort signal received. Excerpt from crash dump: dyld`__abort_with_payload: 0x18f22b4b0 &lt;+0&gt;: mov x16, #0x209 0x18f22b4b4 &lt;+4&gt;: svc #0x80 -&gt; 0x18f22b4b8 &lt;+8&gt;: b.lo 0x18f22b4d8 Console dyld[2080]: Symbol not found: _$s16FoundationModels20LanguageModelSessionC5model10guardrails5tools12instructionsAcA06SystemcD0C_AC10GuardrailsVSayAA4Tool_pGAA12InstructionsVSgtcfC Referenced from: /private/var/containers/Bundle/Application/.../SwiftTranscriptionSampleApp.app/SwiftTranscriptionSampleApp.debug.dylib Expected in: /System/Library/Frameworks/FoundationModels.framework/FoundationModels Question Is this crash expected on Intel Macs and older iPhone models with the beta SDKs? Is there an official statement on whether macOS 26.x releases support Intel, or it exists only until macOS 26.1? Any suggested workarounds for testing this sample project on current hardware? Is this a known limitation for the 26.0 beta, and if so, should we expect a fix in 26.0 or only in subsequent releases? Attaching screenshots for reference. Thank you in advance.

I am excited to try Foundation Models during WWDC, but it doesn't work at all for me. When running on my iPad Pro M4 with iPadOS 26 seed 1, I get the following error even when running the simplest query: let prompt = "How are you?" let stream = session.streamResponse(to: prompt) for try await partial in stream { self.answer = partial self.resultString = partial } In the Xcode console, I see the following error: assetsUnavailable(FoundationModels.LanguageModelSession.GenerationError.Context(debugDescription: "Model is unavailable", underlyingErrors: [])) I have verified that Apple Intelligence is enabled on my iPad. Any tips on how can I get it working? I have also submitted this feedback: FB17896752

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 am a new IOS developer, trying to learn to integrate the Apple Foundation Model. my set up is: Mac M1 Pro MacOS 26 Beta Version 26.0 beta 3 Apple Intelligence &amp; Siri --&gt; On here is the code, func generate() { Task { isGenerating = true output = "⏳ Thinking..." do { let session = LanguageModelSession( instructions: """ Extract time from a message. Example Q: Golfing at 6PM A: 6PM """) let response = try await session.respond(to: "Go to gym at 7PM") output = response.content } catch { output = "❌ Error:, \(error)" print(output) } isGenerating = false } and I get these errors guardrailViolation(FoundationModels.LanguageModelSession.GenerationError.Context(debugDescription: "Prompt may contain sensitive or unsafe content", underlyingErrors: [Asset com.apple.gm.safety_embedding_deny.all not found in Model Catalog])) Can you help me get through this?

I'm using Xcode 26 Beta 5 and get errors on any generation I try, however harmless, when wrapped in the #Playground macro. #Playground { let session = LanguageModelSession() let topic = "pandas" let prompt = "Write a safe and respectful story about (topic)." let response = try await session.respond(to: prompt) Not seeing any issues on simulator or device. Anyone else seeing this or have any ideas? Thanks for any help! Version 26.0 beta 5 (17A5295f) macOS 26.0 Beta (25A5316i)

I've run into an issue with a small Foundation Models test with Generable. I'm getting a strange error message with this Generable. I was able to get simpler ones to work. Is this because the Generable is recursive with a property of [HTMLDiv]? The error message is: FoundationModels/SchemaAugmentor.swift:209: Fatal error: 'try!' expression unexpectedly raised an error: FoundationModels.GenerationSchema.SchemaError.undefinedReferences(schema: Optional("SafeResponse<HTMLDiv>"), references: ["HTMLDiv"], context: FoundationModels.GenerationSchema.SchemaError.Context(debugDescription: "Undefined types: [HTMLDiv]", underlyingErrors: [])) The code is: import FoundationModels import Playgrounds @Generable struct HTMLDiv { @Guide(description: "Optional named ID, useful for nicknames") var id: String? = nil @Guide(description: "Optional visible HTML text") var textContent: String? = nil @Guide(description: "Any child elements", .count(0...10)) var children: [HTMLDiv] = [] static var sample: HTMLDiv { HTMLDiv( id: "profileToolbar", children: [ HTMLDiv(textContent: "Log in"), HTMLDiv(textContent: "Sign up"), ] ) } } #Playground { do { let session = LanguageModelSession { "Your job is to generate simple HTML markup" "Here is an example response to the prompt: 'Make a profile toolbar':" HTMLDiv.sample } let response = try await session.respond( to: "Make a sign up form", generating: HTMLDiv.self ) print(response.content) } catch { print(error) } }

Trying the Foundation Model framework and when I try to run several sessions in a loop, I'm getting a thrown error that I'm hitting a rate limit. Are these rate limits documented? What's the best practice here? I'm trying to run the models against new content downloaded from a web service where I might get ~200 items in a given download. They're relatively small but there can be that many that want to be processed in a loop.

I have a Generable type with many elements. I am using a stream() to incrementally process the output (Generable.PartiallyGenerated?) content. At the end, I want to pass the final version (not partially generated) to another function. I cannot seem to find a good way to convert from a MyGenerable.PartiallyGenerated to a MyGenerable. Am I missing some functionality in the APIs?

Hi, DataScannerViewController does't recognize currencies less than 1.00 (e.g. 0.59 USD, 0.99 EUR, etc.). Why? How to solve the problem? This feature is not described in Apple documentation, is there a solution? This is my code: func makeUIViewController(context: Context) -&gt; DataScannerViewController { let dataScanner = DataScannerViewController(recognizedDataTypes: [ .text(textContentType: .currency)]) return dataScanner }

Hi For certain tasks, such as qualitative analysis or tagging, it is advisable to provide the AI with the option to respond with a joker / wild card answer when it encounters difficulties in tagging or scoring. For instance, you can include this slot in the prompt as follows: output must be "not data to score" when there isn't information to score. In the absence of these types of slots, AI trends to provide a solution even when there is insufficient information. Foundations Models are told to be prompted with simple prompts. I wonder: Is recommended keep this slot though adds verbose complexity? Is the best place the comment of a guided attribute? other tips? Another use case is when you want the AI to be tied to the information provided in the prompt and not take information from its data set. What is the best approach to this purpose? Thanks in advance for any suggestion.

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'

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.