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litert-community/
Phi-4-mini-instruct

Name: Phi-4-mini-instruct
Availability: InStock

tflite

chat

text-generation

microsoft/Phi-4-mini-instruct

finetune

mit

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litert-community/Phi-4-mini-instruct

This model provides a few variants of microsoft/Phi-4-mini-instruct that are ready for deployment on Android using the LiteRT (fka TFLite) stack, MediaPipe LLM Inference API and LiteRT-LM.

Use the models

Colab

Disclaimer: The target deployment surface for the LiteRT models is Android/iOS/Web and the stack has been optimized for performance on these targets. Trying out the system in Colab is an easier way to familiarize yourself with the LiteRT stack, with the caveat that the performance (memory and latency) on Colab could be much worse than on a local device.

Open In Colab

Android

Edge Gallery App

Download or build the app from GitHub.
Install the app from Google Play.
Follow the instructions in the app.

LLM Inference API

Download and install the apk.
Follow the instructions in the app.

To build the demo app from source, please follow the instructions from the GitHub repository.

Performance

Android

Note that all benchmark stats are from a Samsung S24 Ultra with 1280 KV cache size with multiple prefill signatures enabled.

Backend	Quantization scheme	Context length	Prefill (tokens/sec)	Decode (tokens/sec)	Time-to-first-token (sec)	Model size (MB)	Peak RSS Memory (MB)	GPU Memory (MB)
CPU	dynamic_int8	4096	66.53 tk/s	7.28 tk/s	15.90 s	3906 MB	5308 MB	N/A
GPU	dynamic_int8	4096	314.01 tk/s	10.39 tk/s	10.32 s	3906 MB	4107 MB	4608 MB

Model Size: measured by the size of the .tflite flatbuffer (serialization format for LiteRT models)
Memory: indicator of peak RAM usage
The inference on CPU is accelerated via the LiteRT XNNPACK delegate with 4 threads
Benchmark is done assuming XNNPACK cache is enabled
Benchmark is run with cache enabled and initialized. During the first run, the time to first token may differ.
dynamic_int8: quantized model with int8 weights and float activations.