Hello, Could you please provide details for maximum string length of the prompt and the title when using ImageCreator and the method extracted(from:title:)? static func extracted( from text: String, title: String? = nil ) -> ImagePlaygroundConcept Any additional details or example of prompt and title would help. Additionally, are ImagePlaygroundStyle.animation, ImagePlaygroundStyle.illustration and ImagePlaygroundStyle.sketch all available when using extracted(from:title:)? I am trying to generate images programmatically and would appreciate your guidance. Thank you.

Subject: Technical Report: Float32 Precision Ceiling & Memory Fragmentation in JAX/Metal Workloads on M3 To: Metal Developer Relations Hello, I am reporting a repeatable numerical saturation point encountered during sustained recursive high-order differential workloads on the Apple M3 (16 GB unified memory) using the JAX Metal backend. Workload Characteristics: Large-scale vector projections across multi-dimensional industrial datasets Repeated high-order finite-difference calculations Heavy use of jax.grad and lax.cond inside long-running loops Observation: Under these conditions, the Metal/MPS backend consistently enters a terminal quantization lock where outputs saturate at a fixed scalar value (2.0000), followed by system-wide NaN propagation. This appears to be a precision-limited boundary in the JAX-Metal bridge when handling high-order operations with cubic time-scale denominators. have identified the specific threshold where recursive high-order tensor derivatives exceed the numerical resolution of 32-bit consumer architectures, necessitating a migration to a dedicated 64-bit industrial stack. I have prepared a minimal synthetic test script (randomized vectors only, no proprietary logic) that reliably reproduces the allocator fragmentation and saturation behavior. Let me know if your team would like the telemetry for XLA/MPS optimization purposes. Best regards, Alex Severson Architect, QuantumPulse AI

Looking for help with or to help with, due to the pending document enhancement, the Vibe Coders edition of cml editor. Also for more information on how to use the .mlkey whether or not my model is suppose to say IOs18 when I am planning to use it on Mac Apple Intelligence seems to think coreML is for iOS but are the capabilities extended when running NPU on the book? How to use this graph. coming in hot sorry. btw. there are 100s of feedback and crash reports sent in form me for additional info? I attached a image that might help with updating Tags

I've created a "Transfer Learning BERT Embeddings" model with the default "Latin" language family and "Automatic" Language setting. This model performs exceptionally well against the test data set and functions as expected when I preview it in Create ML. However, when I add it to the Xcode project of the application to which I am deploying it, I am getting runtime errors that suggest it can't find the embedding resources: Failed to locate assets for 'mul_Latn' - '5C45D94E-BAB4-4927-94B6-8B5745C46289' embedding model Note, I am adding the model to the app project the same way that I added an earlier "Maximum Entropy" model. That model had no runtime issues. So it seems there is an issue getting hold of the embeddings at runtime. For now, "runtime" means in the Simulator. I intend to deploy my application to iOS devices once GM 26 is released (the app also uses AFM). I'm developing on Tahoe 26 beta, running on iOS 26 beta, using Xcode 26 beta. Is this a known/expected issue? Are the embeddings expected to be a resource in the model? Is there a workaround? I did try opening the model in Xcode and saving it as an mlpackage, then adding that to my app project, but that also didn't resolve the issue.

When doing some exploratory research into using Apple Intelligence in our aviation-focused application, I noticed that there were several times that key phases would be marked as inappropriate. I tried to stifle these using prompts and rules but couldn't get it to take hold. I was encouraged by an Apple employee to go ahead and post this so that the AI team can use the feedback. There were several terms that triggered this warning, but the two that were most prominent were: 'Tailwind' 'JFK' or 'KJFK' (NY airport ICAO/IATA codes)

I'm adding Visual Intelligence support to my app, and now want to add a Tip using TipKit to guide users to this feature from within my app. I want to add a Rule to my Tip which will only show this Tip on devices where Visual Intelligence is supported (ex. not iPhone 14 Pro Max). What is the best way for me to determine availability to set this TipKit rule? Here's the documentation I'm following for Visual Intelligence: https://aninterestingwebsite.com/documentation/visualintelligence/integrating-your-app-with-visual-intelligence

I downloaded the new developer beta and then installed xcode. I did the downloads but I couldn't download the Predictive Code Completion Model. When I try to download it I get the error "The operation couldn’t be completed. (ModelCatalog.CatalogErrors.AssetErrors error 1.)". I am using the M3 Pro model.

Hi all, I am interested in unlocking unique applications with the new foundational models. I have a few questions regarding the availability of the following features: Image Input: The update in June 2025 mentions "image" 44 times (https://machinelearning.apple.com/research/apple-foundation-models-2025-updates) - however I can't seem to find any information about having images as the input/prompt for the foundational models. When will this be available? I understand that there are existing Vision ML APIs, but I want image input into a multimodal on-device LLM (VLM) instead for features like "Which player is holding the ball in the image", etc (image understanding) Cloud Foundational Model - when will this be available? Thanks! Clement :)

JAX Metal shows 55x slower random number generation compared to NVIDIA CUDA on equivalent workloads. This makes Monte Carlo simulations and scientific computing impractical on Apple Silicon. Performance Comparison NVIDIA GPU: 0.475s for 12.6M random elements M1 Max Metal: 26.3s for same workload Performance gap: 55x slower Environment Apple M1 Max, 64GB RAM, macOS Sequoia Version 15.6.1 JAX 0.4.34, jax-metal latest Backend: Metal Reproduction Code import time import jax import jax.numpy as jnp from jax import random key = random.PRNGKey(42) start_time = time.time() random_array = random.normal(key, (50000, 252)) duration = time.time() - start_time print(f"Duration: {duration:.3f}s")

Hardware: Macbook Pro M4 Nov 2024 Software: macOS Tahoe 26.0 & xcode 26.0 Apple Intelligence is activated and the Image playground macOS app works Running the following on xcode throws ImagePlayground.ImageCreator.Error.creationFailed Any suggestions on how to make this work? import Foundation import ImagePlayground Task { let creator = try await ImageCreator() guard let style = creator.availableStyles.first else { print("No styles available") exit(1) } let images = creator.images( for: [.text("A cat wearing mittens.")], style: style, limit: 1) for try await image in images { print("Generated image: \(image)") } exit(0) } RunLoop.main.run()

I followed below url for converting Llama-3.1-8B-Instruct model but always fails even i have 64GB of free space after downloading model from huggingface. https://machinelearning.apple.com/research/core-ml-on-device-llama Also tried with other models Llama-3.1-1B-Instruct & Llama-3.1-3B-Instruct models those are converted but while doing performance test in xcode fails for all compunits. Is there any source code to run llama models in ios app.

I have been working on a small CV program, which uses fine-tuned U2Netp model converted by coremltools 8.3.0 from PyTorch. It works well on my iPhone (with iOS version 18.5) and my Macbook (with MacOS version 15.3.1). But it fails to load after I upgraded Macbook to MacOS version 15.5. I have attached console log when loading this model. Unable to load MPSGraphExecutable from path /Users/yongzhang/Library/Caches/swiftmetal/com.apple.e5rt.e5bundlecache/24F74/E051B28C6957815C140A86134D673B5C015E79A1460E9B54B8764F659FDCE645/16FA8CF2CDE66C0C427F4B51BBA82C38ACC44A514CCA396FD7B281AAC087AB2F.bundle/H14C.bundle/main/main_mps_graph/main_mps_graph.mpsgraphpackage @ GetMPSGraphExecutable E5RT: Unable to load MPSGraphExecutable from path /Users/yongzhang/Library/Caches/swiftmetal/com.apple.e5rt.e5bundlecache/24F74/E051B28C6957815C140A86134D673B5C015E79A1460E9B54B8764F659FDCE645/16FA8CF2CDE66C0C427F4B51BBA82C38ACC44A514CCA396FD7B281AAC087AB2F.bundle/H14C.bundle/main/main_mps_graph/main_mps_graph.mpsgraphpackage (13) Unable to load MPSGraphExecutable from path /Users/yongzhang/Library/Caches/swiftmetal/com.apple.e5rt.e5bundlecache/24F74/E051B28C6957815C140A86134D673B5C015E79A1460E9B54B8764F659FDCE645/16FA8CF2CDE66C0C427F4B51BBA82C38ACC44A514CCA396FD7B281AAC087AB2F.bundle/H14C.bundle/main/main_mps_graph/main_mps_graph.mpsgraphpackage @ GetMPSGraphExecutable E5RT: Unable to load MPSGraphExecutable from path /Users/yongzhang/Library/Caches/swiftmetal/com.apple.e5rt.e5bundlecache/24F74/E051B28C6957815C140A86134D673B5C015E79A1460E9B54B8764F659FDCE645/16FA8CF2CDE66C0C427F4B51BBA82C38ACC44A514CCA396FD7B281AAC087AB2F.bundle/H14C.bundle/main/main_mps_graph/main_mps_graph.mpsgraphpackage (13) Failure translating MIL->EIR network: Espresso exception: "Network translation error": MIL->EIR translation error at /Users/yongzhang/CLionProjects/ImageSimilarity/models/compiled/u2netp.mlmodelc/model.mil:1557:12: Parameter binding for axes does not exist. [Espresso::handle_ex_plan] exception=Espresso exception: "Network translation error": MIL->EIR translation error at /Users/yongzhang/CLionProjects/ImageSimilarity/models/compiled/u2netp.mlmodelc/model.mil:1557:12: Parameter binding for axes does not exist. status=-14 Failed to build the model execution plan using a model architecture file '/Users/yongzhang/CLionProjects/ImageSimilarity/models/compiled/u2netp.mlmodelc/model.mil' with error code: -14.

Hi From https://aninterestingwebsite.com/metal/jax/ I checked all active workflows on https://github.com/jax-ml/jax and any open issues with tags Metal and seems in DEC 2025 the Jax maintainers have closed all issues citing No active development on Jax-metal and the project seems dead. We need to know how can we leverage Apple silicon for accelerated projects using popular academia library and tools . Is the JAX project still going to be supported or Apple has plans to bring something of tis own that might be platform agnostic . Thanks

Hi everyone, I'm trying to use VNDetectTextRectanglesRequest to detect text rectangles in an image. Here's my current code: guard let cgImage = image.cgImage(forProposedRect: nil, context: nil, hints: nil) else { return } let textDetectionRequest = VNDetectTextRectanglesRequest { request, error in if let error = error { print("Text detection error: \(error)") return } guard let observations = request.results as? [VNTextObservation] else { print("No text rectangles detected.") return } print("Detected \(observations.count) text rectangles.") for observation in observations { print(observation.boundingBox) } } textDetectionRequest.revision = VNDetectTextRectanglesRequestRevision1 textDetectionRequest.reportCharacterBoxes = true let handler = VNImageRequestHandler(cgImage: cgImage, orientation: .up, options: [:]) do { try handler.perform([textDetectionRequest]) } catch { print("Vision request error: \(error)") } The request completes without error, but no text rectangles are detected — the observations array is empty (count = 0). Here's a sample image I'm testing with: I expected VNTextObservation results, but I'm not getting any. Is there something I'm missing in how this API works? Or could it be a limitation of this request or revision? Thanks for any help!

Hi everyone, I'm experiencing an inconsistent behavior with the Translation framework on iOS 18. The LanguageAvailability.status() API reports language models as .installed, but translation fails with Code 16. Setup: Using translationTask modifier with TranslationSession Batch translation with explicit source/target languages Languages: Portuguese→English, German→English Issue: let status = await LanguageAvailability().status(from: sourceLang, to: targetLang) // Returns: .installed // But translation fails: let responses = try await session.translations(from: requests) // Error: TranslationErrorDomain Code=16 "Offline models not available" Logs: Language model installed: pt -> en Language model installed: de -> en Starting translation: de -> en Error Domain=TranslationErrorDomain Code=16 "Translation failed"NSLocalizedFailureReason=Offline models not available for language pair What I've tried: Re-downloading languages in Settings Using source: nil for auto-detection Fresh TranslationSession.Configuration each time Questions: Is there a way to force model re-validation/re-download programmatically? Should translationTask show download popup when Code 16 occurs? Has anyone found a reliable workaround? I've seen similar reports in threads 791357 and 777113. Any guidance appreciated! Thanks!

Recently, I'm trying to deploy some third-party LLM to Apple devices. The methodoloy is similar to https://github.com/Anemll/Anemll. The biggest issue I'm having now is the runtime memory usage. When there are multiple functions in a model (mlpackage or mlmodelc), the runtime memory usage for weights is somehow duplicated when I load all of them. Here's the detail: I created my multifunction mlpackage following https://apple.github.io/coremltools/docs-guides/source/multifunction-models.html I loaded each of the functions using the generated swift class: let config = MLModelConfiguration() config.computeUnits = MLComputeUnits.cpuAndNeuralEngine config.functionName = "infer_512"; let ffn1_infer_512 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) config.functionName = "infer_1024"; let ffn1_infer_1024 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) config.functionName = "infer_2048"; let ffn1_infer_2048 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) I observed that RAM usage increases linearly as I load each of the functions. Using instruments, I see that there are multiple HWX files generated and loaded, each of which contains all the weight data. My understanding of what's happening here: The CoreML framework did some MIL->MIL preprocessing before further compilation, which includes separating CPU workload from ANE workload. The ANE part of each function is moved into a separate MIL file then compile separately into a HWX file each. The problem is that the weight data of these HWX files are duplicated. Since that the weight data of LLMs is huge, it will cause out-of-memory issue on mobile devices. The improvement I'm hoping from Apple: I hope we can try to merge the processed MIL files back into one before calling ANECCompile(), so that the weights can be merged. I don't have control over that in user space and I'm not sure if that is feasible. So I'm asking for help here. Thanks.

I get the following error when running this command in a Jupyter notebook: v = tf.Variable(initial_value=tf.random.normal(shape=(3, 1))) v[0, 0].assign(3.) Environment: python == 3.11.14 tensorflow==2.19.1 tensorflow-metal==1.2.0 { "name": "InvalidArgumentError", "message": "Cannot assign a device for operation ResourceStridedSliceAssign: Could not satisfy explicit device specification '/job:localhost/replica:0/task:0/device:GPU:0' because no supported kernel for GPU devices is available.\nColocation Debug Info:\nColocation group had the following types and supported devices: \nRoot Member(assigned_device_name_index_=1 requested_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' assigned_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' resource_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' supported_device_types_=[CPU] possible_devices_=[]\nResourceStridedSliceAssign: CPU \n_Arg: GPU CPU \n\nColocation members, user-requested devices, and framework assigned devices, if any:\n ref (_Arg) framework assigned device=/job:localhost/replica:0/task:0/device:GPU:0\n ResourceStridedSliceAssign (ResourceStridedSliceAssign) /job:localhost/replica:0/task:0/device:GPU:0\n\nOp: ResourceStridedSliceAssign\n [...] [[{{node ResourceStridedSliceAssign}}]] [Op:ResourceStridedSliceAssign] name: strided_slice/_assign" } It seems like the ResourceStridedSliceAssign operation is not implemented for the GPU

Hi! I'm trying to use the ImagePlayground API in SwiftUI with the .imagePlaygroundSheet modifier. However, when the sheet is shown (in the preview or in the simulator) it displays the following message: "Image Playground is not available. Image Playground is not available on this iPhone.". I'm using an iPhone 16 Pro with iOS 18.3.1 in the Xcode (16.2) Simulator. Anyone else having this problem? How can I fix it?

Hello fellow developers, I'm the founder of a FinTech startup, Cent Capital (https://cent.capital), where we are building an AI-powered financial co-pilot. We're deeply exploring the Apple ecosystem to create a more proactive and ambient user experience. A core part of our vision is to use App Intents and the Shortcuts app to surface personalized financial insights without the user always needing to open our app. For example, suggesting a Shortcut like, "What's my spending in the 'Dining Out' category this month?" or having an App Intent proactively surface an insight like, "Your 'Subscriptions' budget is almost full." My question for the community is about the architectural and user experience best practices for this. How are you thinking about the balance between providing rich, actionable insights via Intents without being overly intrusive or "spammy" to the user? What are the best practices for designing the data model that backs these App Intents for a complex domain like personal finance? Are there specific performance or privacy considerations we should be aware of when surfacing potentially sensitive financial data through these system-level integrations? We believe this is the future of FinTech apps on iOS and would love to hear how other developers are thinking about this challenge. Thanks for your insights!

Greetings, Ive been exerimenting with the new Apple intelligence chat. I want to be able to use my custom LLM and I made that work (I can chat back and forward from the left panel with my server) but I cannot find out how to change the editor contents like chatgpt does. chatgpt is able to change the current editor and, seems like, all files in the pbx. I tried to catch the call with charles with no success. In the OpenIA platform docs it doesnt mention anything that could change the code shown. does anyone know how to achieve this? Is the apple intelliece documentation lacking this features and will it be completed soon? will this features even be open for developers?