microsoft/PhiCookBook
Oct 2024 – Oct 2024
1 Month active
Languages Used
GradleJavaKotlinMarkdownShell
Technical Skills
Android DevelopmentJetpack ComposeLLM IntegrationMediaPipeTFLite
Kinfey
PROFILE
Kinfey
Kinfey Lo developed an end-to-end Android demonstration app for the microsoft/PhiCookBook repository, showcasing on-device large language model inference using the Phi-3.5 tflite model. The project featured a chat interface built with Jetpack Compose, enabling private, offline LLM response generation directly on mobile devices. Kinfey implemented model loading and response handling in Kotlin, and provided detailed documentation on converting and deploying the model for Android. This work delivered a reusable sample that allows customers to evaluate low-latency, on-device LLM capabilities in real-world scenarios, offering a practical foundation for private mobile inference without reliance on cloud-based services.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,781
Activity Months1
Your Network
4173 people
Work History
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 focused on delivering an end-to-end mobile demonstration of on-device LLM capabilities for PhiCookBook. The highlights include a new Android sample app that integrates the Phi-3.5 tflite model, featuring a chat interface, model loading, and response generation, plus documentation on converting and deploying the model. This work provides customers with a private, low-latency LLM experience on-device and establishes a concrete starter for evaluating on-device inference in real-world mobile scenarios.
1 Commits • 1 Features
Oct 1, 2024October 2024 focused on delivering an end-to-end mobile demonstration of on-device LLM capabilities for PhiCookBook. The highlights include a new Android sample app that integrates the Phi-3.5 tflite model, featuring a chat interface, model loading, and response generation, plus documentation on converting and deploying the model. This work provides customers with a private, low-latency LLM experience on-device and establishes a concrete starter for evaluating on-device inference in real-world mobile scenarios.
October 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
GradleJavaKotlinMarkdownShell
Technical Skills
Android DevelopmentJetpack ComposeLLM IntegrationMediaPipeTFLite
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