LLMEval
An example that:
- downloads a huggingface model (phi-2) and tokenizer
- evaluates a prompt
- displays the output as it generates text
Note: this must be built Release, otherwise you will encounter stack overflows.
You will need to set the Team on the LLMEval target in order to build and run on iOS.
Some notes about the setup:
- this downloads models from hugging face so LLMEval -> Signing & Capabilities has the "Outgoing Connections (Client)" set in the App Sandbox
- LLM models are large so this uses the Increased Memory Limit entitlement on iOS to allow ... increased memory limits for devices that have more memory
- The Phi2 4 bit model is small enough to run on some iPhone models
- this can be changed by editing
let modelConfiguration = ModelConfiguration.phi4bit
- this can be changed by editing
Troubleshooting
If the program crashes with a very deep stack trace you may need to build in Release configuration. This seems to depend on the size of the model.
There are a couple options:
- build Release
- force the model evaluation to run on the main thread, e.g. using @MainActor
- build
Cmlxwith optimizations by modifyingmlx/Package.swiftand adding.unsafeOptions(["-O3"]),around line 87
Building in Release / optimizations will remove a lot of tail calls in the C++ layer. These lead to the stack overflows.
See discussion here: https://github.com/ml-explore/mlx-swift-examples/issues/3