When I am doing an uncached load of CoreML model on ANE, I received this warning in Xcode console Type of hiddenStates in function main's I/O contains unknown strides. Using unknown strides for MIL tensor buffers with unknown shapes is not recommended in E5ML. Please use row_alignment_in_bytes property instead. Refer to https://e5-ml.apple.com/more-info/memory-layouts.html for more information. However, the web link does not seem to be working. Where can I find more information about about this and how can I fix it?

I don't know if these forums are any good for rumors or plans, but does anybody know whether or not Apple plans to release a library for training reinforcement learning? It would be handy, implementing games in Swift, for example, to be able to train the computer players on the same code.

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.

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.

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

Is the face and body detection service in the Vision framework a local model or a cloud model? Is there a performance report? https://developer.apple.com/documentation/vision

After running performance test on my CoreML qwen3 vision, I appreciated the update where results were viewable... ON Mac it mentions Ios18 and im not sure if or how to change.. that bottle neck lead to rebuilding CoreML view. I woke up and realized I have all the pieces together... and ended up with a swift package working demo of Clawbot.. the current issue is Im trying to use gguf 3b to code it.. I have become well aware that everything I create using the big models, they soon become the default themes /layouts for everyone else simply asking for this or that (I appoligise) so here I am asking (while looking to schedule meet with dev) if its possible to speak with anyone about th 1000s of Apple Intelligence PCC, Xcode, and vision reports and feedback ive sent , in terms of just general ways I can work more efficiently without the crash... ive already build a TUI for MLX but the tools for coreML while seems promising are not intuitive, but the vision format instruction was nice to see. Anyway my question is:

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!

I'm running MacOs 26 Beta 5. I noticed that I can no longer achieve 100% usage on the ANE as I could before with Apple Foundations on-device model. Has Apple activated some kind of throttling or power limiting of the ANE? I cannot get above 3w or 40% usage now since upgrading. I'm on the high power energy mode. I there an API rate limit being applied? I kave a M4 Pro mini with 64 GB of memory.

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.

Hello, I am interested in using jax-metal to train ML models using Apple Silicon. I understand this is experimental. After installing jax-metal according to https://developer.apple.com/metal/jax/, my python code fails with the following error JaxRuntimeError: UNKNOWN: -:0:0: error: unknown attribute code: 22 -:0:0: note: in bytecode version 6 produced by: StableHLO_v1.12.1 My issue is identical to the one reported here https://github.com/jax-ml/jax/issues/26968#issuecomment-2733120325, and is fixed by pinning to jax-metal 0.1.1., jax 0.5.0 and jaxlib 0.5.0. Thank you!

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 & Siri --> 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?

Hi From https://developer.apple.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

While runninf Apple Foundation Model in iPhone simulator, I got this error: IPC error: Underlying connection interrupted What does this mean? Related to foundation model?

I have used mlx_lm.lora to fine tune a mistral-7b-v0.3-4bit model with my data. I fused the mistral model with my adapters and upload the fused model to my directory on huggingface. I was able to use mlx_lm.generate to use the fused model in Terminal. However, I don't know how to load the model in Swift. I've used Imports import SwiftUI import MLX import MLXLMCommon import MLXLLM let modelFactory = LLMModelFactory.shared let configuration = ModelConfiguration( id: "pharmpk/pk-mistral-7b-v0.3-4bit" ) // Load the model off the main actor, then assign on the main actor let loaded = try await modelFactory.loadContainer(configuration: configuration) { progress in print("Downloading progress: \(progress.fractionCompleted * 100)%") } await MainActor.run { self.model = loaded } I'm getting an error runModel error: downloadError("A server with the specified hostname could not be found.") Any suggestions? Thanks, David PS, I can load the model from the app bundle // directory: Bundle.main.resourceURL! but it's too big to upload for Testflight

Hello, I am studying macOS26 Apple Intelligence features. I have created a basic swift program with Xcode. This program is sending prompts to FoundationModels.LanguageModelSession. It works fine but this model is not trained for programming or code completion. Xcode has an AI code completion feature. It is called "Predictive Code completion model". So, there are multiple on-device models on macOS26 ? Are there others ? Is there a way for me to send prompts to this "Predictive Code completion model" from my program ? Thanks

I have been able to train an adapter on Google's Colaboratory. I am able to start a LanguageModelSession and load it with my adapter. The problem is that after one simple prompt, the context window is 90% full. If I start the session without the adapter, the same simple prompt consumes only 1% of the context window. Has anyone encountered this? I asked Claude AI and it seems to think that my training script needs adjusting. Grok on the other hand is (wrongly, I tried) convinced that I just need to tweak some parameters of LanguageModelSession or SystemLanguageModel. Thanks for any tips.

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?

I’m considering creating an ILMessageFilterExtension using a mini LLM/SLM to detect fraud and I’ve read it has strict memory limits yet I can’t find it in the documentation. What’s the set limit or any other constraints impacting the feasibility of running 100-500mb model?

At WWDC25 we launched a new type of Lab event for the developer community - Group Labs. A Group Lab is a panel Q&A designed for a large audience of developers. Group Labs are a unique opportunity for the community to submit questions directly to a panel of Apple engineers and designers. Here are the highlights from the WWDC25 Group Lab for Apple Intelligence. Can I integrate writing tools in my own text editor? UITextView, NSTextView, and SwiftUI TextEditor automatically get Writing Tools on devices that support Apple Intelligence. For custom text editors, check out Enhancing your custom text engine with Writing Tools. Given that Foundation Models are on-device, how will Apple update the models over time? And how should we test our app against the model updates? Model updates are in sync with OS updates. As for testing with updated models, watch our WWDC session about prompt engineering and safety, and read the Human Interface Guidelines to understand best practices in prompting the on-device model. What is the context size of a session in Foundation Models Framework? How to handle the error if a session runs out of the context size? Currently the context size is about 4,000 tokens. If it’s exceeded, developers can catch the .exceededContextWindowSize error at runtime. As discussed in one of our WWDC25 sessions, when the context window is exceeded, one approach is to trim and summarize a transcript, and then start a new session. Can I do image generation using the Foundation Models Framework or is only text generation supported? Foundation Models do not generate images, but you can use the Foundation Models framework to generate prompts for ImageCreator in the Image Playground framework. Developers can also take advantage of Tools in Foundation Models framework, if appropriate for their app. My app currently uses a third party server-based LLM. Can I use the Foundation Models Framework as well in the same app? Any guidance here? The Foundation Models framework is optimized for a subset of tasks like summarization, extraction, classification, and tagging. It’s also on-device, private, and free. But at 3 billion parameters it isn’t designed for advanced reasoning or world knowledge, so for some tasks you may still want to use a larger server-based model. Should I use the AFM for my language translation features given it does text translation, or is the Translation API still the preferred approach? The Translation API is still preferred. Foundation Models is great for tasks like text summarization and data generation. It’s not recommended for general world knowledge or translation tasks. Will the TranslationSession class introduced in ios18 get any new improvments in performance or reliability with the new live translation abilities in ios/macos/ipados 26? Essentially, will we get access to live translation in a similar way and if so, how? There's new API in LiveCommunicationKit to take advantage of live translation in your communication apps. The Translate framework is using the same models as used by Live Communication and can be combined with the new SpeechAnalyzer API to translate your own audio. How do I set a default value for an App Intent parameter that is otherwise required? You can implement a default value as part of your parameter declaration by using the @Parameter(defaultValue:) form of the property wrapper. How long can an App Intent run? On macOS there is no limit to how long app intents can run. On iOS, there is a limit of 30 seconds. This time limit is paused when waiting for user interaction. How do I vary the options for a specific parameter of an App Intent, not just based on the type? Implement a DynamicOptionsProvider on that parameter. You can add suggestedEntities() to suggest options. What if there is not a schema available for what my app is doing? If an app intent schema matching your app’s functionality isn’t available, take a look to see if there’s a SiriKit domain that meets your needs, such as for media playback and messaging apps. If your app’s functionality doesn’t match any of the available schemas, you can define a custom app intent, and integrate it with Siri by making it an App Shortcut. Please file enhancement requests via Feedback Assistant for new App intent schemas that would benefit your app. Are you adding any new app intent domains this year? In addition to all the app intent domains we announced last year, this year at WWDC25 we announced that Visual Intelligence will be added to iOS 26 and macOS Tahoe. When my App Intent doesn't show up as an action in Shortcuts, where do I start in figuring out what went wrong? App Intents are statically extracted. You can check the ExtractMetadata info in Xcode's build log. What do I need to do to make sure my App Intents work well with Spotlight+? Check out our WWDC25 sessions on App Intents, including Explore new advances in App Intents and Develop for Shortcuts and Spotlight with App Intents. Mostly, make sure that your intent can run from the parameter summary alone, no required parameters without default values that are not already in the parameter summary. Does Spotlight+ on macOS support App Shortcuts? Not directly, but it shows the App Intents your App Shortcuts are sitting on top of. I’m wondering if the on-device Foundation Models framework API can be integrated into an app to act strictly as an app in-universe AI assistant, responding only within the boundaries of the app’s fictional context. Is such controlled, context-limited interaction supported? FM API runs inside the process of your app only and does not automatically integrate with any remaining part of the system (unless you choose to implement your own tool and utilize tool calling). You can provide any instructions and prompts you want to the model. If a country does not support Apple Intelligence yet, can the Foundation Models framework work? FM API works on Apple Intelligence-enabled devices in supported regions and won’t work in regions where Apple Intelligence is not yet supported