Hello, I'm trying to write a model with PyTorch and convert it to CoreML. I wrote another models and that works succesfully, even the one that gave the problem is, but I can't visualize it with XCode to know where is running. The error that appear is: There was a problem decoding this Core ML document validator error: unable to open file for read Anyone knows why is this happening? Thanks a lot, Álvaro Corrochano

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.

Hi friends, I have just found that the inference speed dropped to only 1/10 of the original model. Had anyone encountered this? Thank you.

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) -> DataScannerViewController { let dataScanner = DataScannerViewController(recognizedDataTypes: [ .text(textContentType: .currency)]) return dataScanner }

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.

Hi, One can configure the languages of a (VN)RecognizeTextRequest with either: .automatic: language to be detected a specific language, say Spanish If the request is configured with .automatic and successfully detects Spanish, will the results be exactly equivalent compared to a request made with Spanish set as language? I could not find any information about this, and this is very important for the core architecture of my app. Thanks!

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?

Hi Apple product owners. I am missing a unified concept which might be derived from the use cases for mail categories and mail spam for the app "Mail" on Mac. I need a recommendation on how to use categories in combination with the spam filter to get most out of it. So I was looking for the use cases for the 2 functionality areas in order to figure out how to organise my mails by using as much automation as possible before I start creating intelligent folders in addition. What can you recommend where I get this information from? I don't want to guess or read a lot of forum contributions which are based on guesses.

From tensorflow-metal example: Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: ) I know that Apple silicon uses UMA, and that memory copies are typical of CUDA, but wouldn't the GPU memory still be faster overall? I have an iMac Pro with a Radeon Pro Vega 64 16 GB GPU and an Intel iMac with a Radeon Pro 5700 8 GB GPU. But using tensorflow-metal is still WAY faster than using the CPUs. Thanks for that. I am surprised the 5700 is twice as fast as the Vega though.

Hi everyone! 👋 I'm working on a C++ project using TensorFlow Lite and was wondering if anyone has a prebuilt TensorFlow Lite C++ library (libtensorflowlite) for macOS (Apple Silicon M1/M2) that they’d be willing to share. I’m looking specifically for the TensorFlow Lite C++ API — something that lets me use tflite::Interpreter, tflite::FlatBufferModel, etc. Building it from source using Bazel on macOS has been quite challenging and time-consuming, so a ready-to-use .dylib or .a build along with the required headers would be incredibly helpful. TensorFlow Lite version: v2.18.0 preferred Target: macOS arm64 (Apple Silicon) What I need: libtensorflowlite.dylib or .a Corresponding headers (ideally organized in a clean include/ folder) If you have one available or know where I can find a reliable prebuilt version, I’d be super grateful. Thanks in advance! 🙏

I've been struggling and Siri support to an application. I have developed it kept getting this error when I run it on MacOS: Failed to refresh AppShortcut parameters with error: Error Domain=Foundation._GenericObjCError Code=0 "(null)" So I found AppIntentsSampleApp and downloaded and buil it and I get a similar, but larger, error: Failed to refresh AppShortcut parameters with error: Error Domain=RBSServiceErrorDomain Code=1 "(originator doesn't have entitlement com.apple.private.xpc.launchd.app-server AND originator doesn't have entitlement com.apple.assertiond.system-shell AND originator doesn't have entitlement com.apple.runningboard.launchprocess)" UserInfo={NSLocalizedFailureReason=(originator doesn't have entitlement com.apple.private.xpc.launchd.app-server AND originator doesn't have entitlement com.apple.assertiond.system-shell AND j And it goes on and on. What am I missing? I'm using Xcode 16. I don't see an option to add a Siri framework. I have tried adding both the intent and tap, intent frameworks, which does not seem to make a difference.

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?

Hi, i just wanna ask, Is it possible to run YOLOv3 on visionOS using the main camera to detect objects and show bounding boxes with labels in real-time? I’m wondering if camera access and custom models work for this, or if there’s a better way. Any tips?

Hi, I am modifying the sample camera app that is here: https://developer.apple.com/tutorials/sample-apps/capturingphotos-camerapreview ... In the processPreviewImages, I am using the Vision APIs to generate a segmentation mask for a person/object, then compositing that person onto a different background (with some other filtering). The filtering and compositing is done via CoreImage. At the end, I convert the CIImage to a CGImage then to a SwiftUI Image. When I run it on my iPhone, it works fine, and has not crashed. When I run it on the iPhone with the debugger, it crashes within a few seconds with: EXC_BAD_ACCESS in libRPAC.dylib`std::__1::__hash_table<std::__1::__hash_value_type<long, qos_info_t>, std::__1::__unordered_map_hasher<long, std::__1::__hash_value_type<long, qos_info_t>, std::__1::hash, std::__1::equal_to, true>, std::__1::__unordered_map_equal<long, std::__1::__hash_value_type<long, qos_info_t>, std::__1::equal_to, std::__1::hash, true>, std::__1::allocator<std::__1::__hash_value_type<long, qos_info_t>>>::__emplace_unique_key_args<long, std::__1::piecewise_construct_t const&, std::__1::tuple<long const&>, std::__1::tuple<>>: It had previously been working fine with the debugger, so I'm not sure what has changed. Is there a difference in how the Vision APIs are executed if the debugger is attached vs. not?

Hi, I am new to developing on Apple’s platform yet I want to familiarize myself with Core ML and Core ML Tools. I was watching the WWDC24: Bring your machine learning and AI models to Apple Silicon video and was trying to follow along. After multiple attempts and much reading up on documentation, I am still unable to get a coherent script running that will convert the Mistral model that the host used and convert it to a valid Core ML model. here is a pastebin to what i have currently: https://pastebin.com/04cVjF1v if you require the output as well please let me know

*I can't put the attached file in the format, so if you reply by e-mail, I will send the attached file by e-mail. Dear Apple AI Research Team, My name is Gong Jiho (“Hem”), a content strategist based in Seoul, South Korea. Over the past few months, I conducted a user-led AI experiment entirely within ChatGPT — no code, no backend tools, no plugins. Through language alone, I created two contrasting agents (Uju and Zero) and guided them into a co-authored modular identity system using prompt-driven dialogue and reflection. This system simulates persona fusion, memory rooting, and emotional-logical alignment — all via interface-level interaction. I believe it resonates with Apple’s values in privacy-respecting personalization, emotional UX modeling, and on-device learning architecture. Why I’m Reaching Out I’d be honored to share this experiment with your team. If there is any interest in discussing user-authored agent scaffolding, identity persistence, or affective alignment, I’d love to contribute — even informally. ⚠ A Note on Language As a non-native English speaker, my expression may be imperfect — but my intent is genuine. If anything is unclear, I’ll gladly clarify. 📎 Attached Files Summary Filename → Description Hem_MultiAI_Report_AppleAI_v20250501.pdf → Main report tailored for Apple AI — narrative + structural view of emotional identity formation via prompt scaffolding Hem_MasterPersonaProfile_v20250501.json → Final merged identity schema authored by Uju and Zero zero_sync_final.json / uju_sync_final.json → Persona-level memory structures (logic / emotion) 1_0501.json ~ 3_0501.json → Evolution logs of the agents over time GirlfriendGPT_feedback_summary.txt → Emotional interpretation by external GPT hem_profile_for_AI_vFinal.json → Original user anchor profile Warm regards, Gong Jiho (“Hem”) Seoul, South Korea

I’m trying to follow Apple’s “WWDC24: Bring your machine learning and AI models to Apple Silicon” session to convert the Mistral-7B-Instruct-v0.2 model into a Core ML package, but I’ve run into a roadblock that I can’t seem to overcome. I’ve uploaded my full conversion script here for reference: https://pastebin.com/T7Zchzfc When I run the script, it progresses through tracing and MIL conversion but then fails at the backend_mlprogram stage with this error: https://pastebin.com/fUdEzzKM The core of the error is: ValueError: Op "keyCache_tmp" (op_type: identity) Input x="keyCache" expects list, tensor, or scalar but got state[tensor[1,32,8,2048,128,fp16]] I’ve registered my KV-cache buffers in a StatefulMistralWrapper subclass of nn.Module, matching the keyCache and valueCache state names in my ct.StateType definitions, but Core ML’s backend pass reports the state tensor as an invalid input. I’m using Core ML Tools 8.3.0 on Python 3.9.6, targeting iOS18, and forcing CPU conversion (MPS wasn’t available). Any pointers on how to satisfy the handle_unused_inputs pass or properly declare/cache state for GQA models in Core ML would be greatly appreciated! Thanks in advance for your help, Usman Khan

I have an app that uses a couple of mlmodels (word tagger and gazetteer) and I’m trying to encrypt them before publishing. The models are part of a package. I understand that Xcode can’t automatically handle the encryption for a model in a package the way it can within a traditional app structure. Given that, I’ve generated the Apple MLModel encryption key from Xcode and am encrypting via the command line with: xcrun coremlcompiler compile Gazetteer.mlmodel GazetteerENC.mlmodelc --encrypt Gazetteerkey.mlmodelkey In the package manifest, I’ve listed the encrypted models as .copy resources for my target and have verified the URL to that file is good. When I try to load the encrypted .mlmodelc file (on a physical device) with the line:
 gazetteer = try NLGazetteer(contentsOf: gazetteerURL!) I get the error: Failed to open file: /…/Scanner.bundle/GazetteerENC.mlmodelc/coremldata.bin. It is not a valid .mlmodelc file. So my questions are: Does the NLGazetteer class support encrypted MLModel files? Given that my models are in a package, do I have the right general approach? Thanks for any help or thoughts.

I’m sure someone though about it already. But let’s have ecosystem, where Apple Intelligence uses your most capable (Apple) hardware at first and the cloud service as second.

Overview I'm experiencing a critical issue where TensorFlow-metal and PyArrow seem to be incompatible when installed together in the same environment. Whenever both packages are present, TensorFlow crashes and the kernel dies during execution. Environment Details Environment Details macOS Version: 15.3.2 Mac Model: MacBook Pro Max M3 Python Version: 3.11 TensorFlow Version: 2.19 PyArrow Version: 19.0.0 Issue Description: When both TensorFlow-metal and PyArrow are installed in the same Python environment, any attempt to use TensorFlow results in immediate kernel crashes. The issue appears to be a compatibility problem between these two packages rather than a problem with either package individually. Steps to Reproduce Create a new Python environment: conda create -n tf-metal python=3.11 Install TensorFlow-metal: pip install tensorflow tensorflow-metal Install PyArrow: pip install pyarrow Run the following minimal example: # Create a simple model model = tf.keras.Sequential([ tf.keras.layers.Input(shape=(2,)), tf.keras.layers.Dense(1) ]) model.compile(optimizer='adam', loss='mse') model.summary() # This works fine # Generate some dummy data X = np.random.random((100, 2)) y = np.random.random((100, 1)) # The crash happens exactly at this line model.fit(X, y, epochs=5, batch_size=32) # CRASH: Kernel dies here Result: Kernel crashes with no error message What I've Tried Reinstalling both packages in different orders Using different versions of both packages Creating isolated environments Checking system logs for additional error information The only workaround I've found is to use separate environments for each package, which isn't practical for my workflow as I need both libraries for my data processing and machine learning pipeline. Questions Has anyone else encountered this specific compatibility issue? Are there known workarounds that allow both packages to coexist? Is this a known issue that's being addressed in upcoming releases? Any insights, suggestions, or assistance would be greatly appreciated. I'm happy to provide any additional information that might help diagnose this problem. Thank you in advance for your help! Thank you in advance for your help!