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* move docs -> source * Add files via upload * mv image * Add files via upload * colocate iOS setup doc * delete image * Add files via upload * fix * delete image * Add files via upload * Update developer_cookbook.md * toctree * wip subfolder * docs update * subfolder * updates * name * updates * index * updates * refactor structure * depth * docs * content * docs * getting started * distributions * fireworks * fireworks * update * theme * theme * theme * pdj theme * pytorch theme * css * theme * agents example * format * index * headers * copy button * test tabs * test tabs * fix * tabs * tab * tabs * sphinx_design * quick start commands * size * width * css * css * download models * asthetic fix * tab format * update * css * width * css * docs * tab based * tab * tabs * docs * style * image * css * color * typo * update docs * missing links * list templates * links * links update * troubleshooting * fix * distributions * docs * fix table * kill llamastack-local-gpu/cpu * Update index.md * Update index.md * mv ios_setup.md * Update ios_setup.md * Add remote_or_local.gif * Update ios_setup.md * release notes * typos * Add ios_setup to index * nav bar * hide torctree * ios image * links update * rename * rename * docs * rename * links * distributions * distributions * distributions * distributions * remove release * remote --------- Co-authored-by: dltn <6599399+dltn@users.noreply.github.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
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# LocalInference
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LocalInference provides a local inference implementation powered by [executorch](https://github.com/pytorch/executorch/).
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Llama Stack currently supports on-device inference for iOS with Android coming soon. You can run on-device inference on Android today using [executorch](https://github.com/pytorch/executorch/tree/main/examples/demo-apps/android/LlamaDemo), PyTorch’s on-device inference library.
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## Installation
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We're working on making LocalInference easier to set up. For now, you'll need to import it via `.xcframework`:
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1. Clone the executorch submodule in this repo and its dependencies: `git submodule update --init --recursive`
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1. Install [Cmake](https://cmake.org/) for the executorch build`
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1. Drag `LocalInference.xcodeproj` into your project
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1. Add `LocalInference` as a framework in your app target
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1. Add a package dependency on https://github.com/pytorch/executorch (branch latest)
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1. Add all the kernels / backends from executorch (but not exectuorch itself!) as frameworks in your app target:
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- backend_coreml
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- backend_mps
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- backend_xnnpack
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- kernels_custom
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- kernels_optimized
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- kernels_portable
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- kernels_quantized
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1. In "Build Settings" > "Other Linker Flags" > "Any iOS Simulator SDK", add:
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```
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-force_load
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$(BUILT_PRODUCTS_DIR)/libkernels_optimized-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libkernels_custom-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libkernels_quantized-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libbackend_xnnpack-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libbackend_coreml-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libbackend_mps-simulator-release.a
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```
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1. In "Build Settings" > "Other Linker Flags" > "Any iOS SDK", add:
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```
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-force_load
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$(BUILT_PRODUCTS_DIR)/libkernels_optimized-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libkernels_custom-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libkernels_quantized-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libbackend_xnnpack-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libbackend_coreml-simulator-release.a
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-force_load
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$(BUILT_PRODUCTS_DIR)/libbackend_mps-simulator-release.a
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```
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## Preparing a model
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1. Prepare a `.pte` file [following the executorch docs](https://github.com/pytorch/executorch/blob/main/examples/models/llama/README.md#step-2-prepare-model)
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2. Bundle the `.pte` and `tokenizer.model` file into your app
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We now support models quantized using SpinQuant and QAT-LoRA which offer a significant performance boost (demo app on iPhone 13 Pro):
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| Llama 3.2 1B | Tokens / Second (total) | | Time-to-First-Token (sec) | |
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| :---- | :---- | :---- | :---- | :---- |
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| | Haiku | Paragraph | Haiku | Paragraph |
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| BF16 | 2.2 | 2.5 | 2.3 | 1.9 |
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| QAT+LoRA | 7.1 | 3.3 | 0.37 | 0.24 |
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| SpinQuant | 10.1 | 5.2 | 0.2 | 0.2 |
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## Using LocalInference
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1. Instantiate LocalInference with a DispatchQueue. Optionally, pass it into your agents service:
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```swift
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init () {
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runnerQueue = DispatchQueue(label: "org.meta.llamastack")
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inferenceService = LocalInferenceService(queue: runnerQueue)
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agentsService = LocalAgentsService(inference: inferenceService)
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}
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```
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2. Before making any inference calls, load your model from your bundle:
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```swift
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let mainBundle = Bundle.main
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inferenceService.loadModel(
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modelPath: mainBundle.url(forResource: "llama32_1b_spinquant", withExtension: "pte"),
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tokenizerPath: mainBundle.url(forResource: "tokenizer", withExtension: "model"),
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completion: {_ in } // use to handle load failures
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)
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```
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3. Make inference calls (or agents calls) as you normally would with LlamaStack:
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```
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for await chunk in try await agentsService.initAndCreateTurn(
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messages: [
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.UserMessage(Components.Schemas.UserMessage(
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content: .case1("Call functions as needed to handle any actions in the following text:\n\n" + text),
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role: .user))
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]
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) {
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```
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## Troubleshooting
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If you receive errors like "missing package product" or "invalid checksum", try cleaning the build folder and resetting the Swift package cache:
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(Opt+Click) Product > Clean Build Folder Immediately
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```
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rm -rf \
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~/Library/org.swift.swiftpm \
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~/Library/Caches/org.swift.swiftpm \
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~/Library/Caches/com.apple.dt.Xcode \
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~/Library/Developer/Xcode/DerivedData
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```
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