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