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pweglikJakubGoneraNorbertKlockiewiczchmjkbmkopcins
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Merge 0.4.0 (#314)
## Description <!-- Provide a concise and descriptive summary of the changes implemented in this PR. --> ### Type of change - [ ] Bug fix (non-breaking change which fixes an issue) - [ ] New feature (non-breaking change which adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected) - [ ] Documentation update (improves or adds clarity to existing documentation) ### Tested on - [ ] iOS - [ ] Android ### Testing instructions <!-- Provide step-by-step instructions on how to test your changes. Include setup details if necessary. --> ### Screenshots <!-- Add screenshots here, if applicable --> ### Related issues <!-- Link related issues here using #issue-number --> ### Checklist - [ ] I have performed a self-review of my code - [ ] I have commented my code, particularly in hard-to-understand areas - [ ] I have updated the documentation accordingly - [ ] My changes generate no new warnings ### Additional notes <!-- Include any additional information, assumptions, or context that reviewers might need to understand this PR. --> --------- Co-authored-by: Jakub Gonera <[email protected]> Co-authored-by: Norbert Klockiewicz <[email protected]> Co-authored-by: Jakub Chmura <[email protected]> Co-authored-by: Mateusz Kopcinski <[email protected]> Co-authored-by: Jakub Mroz <[email protected]> Co-authored-by: kopcion <[email protected]> Co-authored-by: Maciej Rys <[email protected]> Co-authored-by: Maciej Rys <maciej.rys@swmansioncom> Co-authored-by: Mateusz Sluszniak <[email protected]> Co-authored-by: Michał Dydek <[email protected]> Co-authored-by: Mateusz Kopciński <[email protected]> Co-authored-by: Patrycja Kalińska <[email protected]>
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‎.cspell-wordlist.txt‎

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swmansion
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executorch
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execu
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Execu
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torch
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huggingface
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bbox
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bboxes
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deeplab
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unsqueeze
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qlora
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spinquant
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efficientnet
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ssdlite
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udnie
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crnn
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mobilenet
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microcontrollers
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notimestamps
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seqs
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smollm
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qwen
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XNNPACK
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EFFICIENTNET
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SSDLITE
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MOBILENET
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UDNIE
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CRNN
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SPINQUANT
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QLORA
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GGUF
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deeplabv
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DEELABV
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ARGMAX
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Abaza
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Adyghe
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Chech
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Dargwa
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Ingush
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Karbadian
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Lezghian
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Occitan
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Tabassaran
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Sinhala
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Infima
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sublabel
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Aeonik
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Lexend
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finetuned
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MINILM
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MPNET

‎.cspell.json‎

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{
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"version": "0.2",
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"language": "en",
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"ignorePaths": ["**/node_modules", "**/Pods"],
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"dictionaryDefinitions": [
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{
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"name": "project-words",
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"path": ".cspell-wordlist.txt"
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}
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],
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"dictionaries": ["project-words"]
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}

‎.eslintrc‎

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{
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"parserOptions": {
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"requireConfigFile": false,
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"babelOptions": {
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"presets": [
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"@babel/preset-react"
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]
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}
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},
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"root": true,
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"extends": [
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"@react-native",
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"prettier",
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"plugin:@cspell/recommended"
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],
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"rules": {
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"react/react-in-jsx-scope": "off",
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"prettier/prettier": [
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"error",
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{
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"quoteProps": "consistent",
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"singleQuote": true,
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"tabWidth": 2,
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"trailingComma": "es5",
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"useTabs": false
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}
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],
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"@cspell/spellchecker": ["warn", { "customWordListFile": ".cspell-wordlist.txt" }],
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"camelcase": "error"
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},
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"plugins": [
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"eslint-plugin-prettier"
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],
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"ignorePatterns": [
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"node_modules/",
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"lib/"
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]
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}

‎.github/workflows/build-android-llama-example.yml‎ renamed to ‎.github/workflows/build-android-llm-example.yml‎

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name: Llama Example app Android build check
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name: LLM Example app Android build check
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on:
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pull_request:
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paths:
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- .github/workflows/build-android-llama-example.yml
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- .github/workflows/build-android-llm-example.yml
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- android/**
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- third-party/android/**
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- examples/llama/package.json
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- examples/llama/android/**
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- examples/llm/package.json
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- examples/llm/android/**
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push:
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branches:
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- main
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paths:
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- .github/workflows/build-android-llama-example.yml
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- .github/workflows/build-android-llm-example.yml
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- android/**
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- third-party/android/**
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- examples/llama/package.json
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- examples/llama/android/**
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- examples/llm/package.json
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- examples/llm/android/**
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workflow_dispatch:
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jobs:
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build:
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if: github.repository == 'software-mansion/react-native-executorch'
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runs-on: ubuntu-latest
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env:
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WORKING_DIRECTORY: examples/llama
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WORKING_DIRECTORY: examples/llm
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concurrency:
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group: android-${{ github.ref }}
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cancel-in-progress: true
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distribution: 'zulu'
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java-version: 17
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- name: Install node dependencies
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working-directory: ${{ env.WORKING_DIRECTORY }}
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run: yarn install --immutable
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- name: Build app
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working-directory: ${{ env.WORKING_DIRECTORY }}/android

‎.github/workflows/build-ios-llama-example.yml‎ renamed to ‎.github/workflows/build-ios-llm-example.yml‎

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name: Llama Example app iOS build check
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name: LLM Example app iOS build check
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on:
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push:
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branches:
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- main
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paths:
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- '.github/workflows/build-ios-llama-example.yml'
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- '.github/workflows/build-ios-llm-example.yml'
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- '*.podspec'
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- 'examples/llama/ios/**'
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- 'examples/llama/package.json'
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- 'examples/llm/ios/**'
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- 'examples/llm/package.json'
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pull_request:
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paths:
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- '.github/workflows/build-ios-llama-example.yml'
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- '.github/workflows/build-ios-llm-example.yml'
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- '*.podspec'
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- 'examples/llama/ios/**'
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- 'examples/llama/package.json'
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- 'examples/llm/ios/**'
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- 'examples/llm/package.json'
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workflow_dispatch:
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jobs:
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build:
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group: ios-${{ github.ref }}
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cancel-in-progress: true
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steps:
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- uses: maxim-lobanov/setup-xcode@v1
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with:
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xcode-version: latest-stable
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- name: Check out Git repository
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uses: actions/checkout@v4
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- name: Install node dependencies
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working-directory: examples/llama
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working-directory: examples/llm
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run: yarn
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- name: Install pods
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working-directory: examples/llama/ios
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working-directory: examples/llm/ios
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run: pod install
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- name: Build app
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working-directory: examples/llama/ios
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working-directory: examples/llm/ios
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run: |
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set -o pipefail && xcodebuild \
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-workspace llama.xcworkspace \
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-scheme llama \
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-workspace llm.xcworkspace \
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-scheme llm \
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-sdk iphonesimulator \
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-configuration Debug \
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-destination 'platform=iOS Simulator,name=iPhone 16 Pro' \

‎.gitignore‎

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# React Native Codegen
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ios/generated
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android/generated
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# custom
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*.tgz
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Makefile

‎.gitmodules‎

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[submodule "executorch"]
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path = third-party/executorch
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url = https://github.com/software-mansion-labs/executorch
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[submodule "tokenizers-cpp"]
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path = third-party/tokenizers-cpp
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url = https://github.com/software-mansion-labs/tokenizers-cpp

‎.prettierrc‎

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{
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"quoteProps": "consistent",
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"singleQuote": true,
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"tabWidth": 2,
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"trailingComma": "es5",
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"useTabs": false
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}

‎README.md‎

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**ExecuTorch** is a novel framework created by Meta that enables running AI models on devices such as mobile phones or microcontrollers. React Native ExecuTorch bridges the gap between React Native and native platform capabilities, allowing developers to run AI models locally on mobile devices with state-of-the-art performance, without requiring deep knowledge of native code or machine learning internals.
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**Table of contents:**
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- [Compatibility](#compatibility)
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- [Ready-made models 🤖](#readymade-models-)
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- [Documentation 📚](#documentation-)
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- [🦙 Quickstart - Running Llama](#-quickstart---running-llama)
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- [Minimal supported versions](#minimal-supported-versions)
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- [Examples 📲](#examples-)
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- [Warning](#warning)
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- [License](#license)
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- [What's next?](#whats-next)
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## Compatibility
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React Native Executorch supports only the [New React Native architecture](https://reactnative.dev/architecture/landing-page).
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If your app still runs on the old architecture, please consider upgrading to the New Architecture.
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## Readymade models 🤖
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## Ready-made models 🤖
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To run any AI model in ExecuTorch, you need to export it to a `.pte` format. If you're interested in experimenting with your own models, we highly encourage you to check out the [Python API](https://pypi.org/project/executorch/). If you prefer focusing on developing your React Native app, we will cover several common use cases. For more details, please refer to the documentation.
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```tsx
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import {
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LLAMA3_2_3B_QLORA,
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LLAMA3_2_3B_TOKENIZER,
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useLLM,
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LLAMA3_2_1B,
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LLAMA3_2_TOKENIZER_CONFIG,
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} from 'react-native-executorch';
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function MyComponent() {
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// Initialize the model 🚀
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const llama = useLLM({
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modelSource: LLAMA3_2_3B_QLORA,
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tokenizerSource: LLAMA3_2_3B_TOKENIZER,
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modelSource: LLAMA3_2_1B,
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tokenizerSource: LLAMA3_2_TOKENIZER,
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tokenizerConfigSource: LLAMA3_2_TOKENIZER_CONFIG,
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});
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// ... rest of your component
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}
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```tsx
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const handleGenerate = async () => {
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const prompt = 'The meaning of life is';
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// Generate text based on your desired prompt
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const response = await llama.generate(prompt);
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console.log('Llama says:', response);
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const chat = [
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{ role: 'system' content: 'You are a helpful assistant' }
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{ role: 'user', content: 'What is the meaning of life?' }
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];
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// Chat completion
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await llm.generate(chat);
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console.log('Llama says:', llm.response);
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};
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```
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We currently host a few example apps demonstrating use cases of our library:
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- examples/llm - chat application showcasing use of LLMs
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- examples/speech-to-text - Whisper and Moonshine models ready for transcription tasks
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- examples/computer-vision - computer vision related tasks
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- examples/llama - chat applications showcasing use of LLMs
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- examples/text-embeddings - computing text representations for semantic search
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If you would like to run it, navigate to it's project directory, for example `examples/llama` from the repository root and install dependencies with:
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If you would like to run it, navigate to it's project directory, for example `examples/llm` from the repository root and install dependencies with:
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```bash
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yarn

‎android/build.gradle‎

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implementation "com.facebook.react:react-android:+"
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implementation 'org.opencv:opencv:4.10.0'
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implementation "org.jetbrains.kotlin:kotlin-stdlib:$kotlin_version"
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implementation 'com.github.software-mansion:react-native-executorch:main-SNAPSHOT'
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implementation(files("libs/executorch.aar"))
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implementation 'org.opencv:opencv:4.10.0'
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implementation("com.squareup.okhttp3:okhttp:4.9.2")
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}

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