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Sponsored by
PoYo.AI
litert-community / Phi-4-mini-instruct
Model's Last Updated: September 23 2025
text-generation
Introduction of Phi-4-mini-instruct
Model Details of Phi-4-mini-instruct
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 stack and MediaPipe LLM Inference API .
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.
Android
- 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, 512 tokens prefill, 128 tokens decode on CPU.
| Prefill (tokens/sec) | Decode (tokens/sec) | Time-to-first-token (sec) | Memory (RSS in MB) | Model size (MB) | |
|---|---|---|---|---|---|
|
dynamic_int8 |
80 |
23 |
2 |
6,884 |
3,940 |
- Model Size: measured by the size of the .tflite flatbuffer (serialization format for LiteRT models)
- Memory: indicator of peak RAM usage
- The inference is run on CPU is accelerated via the LiteRT XNNPACK delegate with 4 threads
- Benchmark is done assuming XNNPACK cache is enabled
- dynamic_int8: quantized model with int8 weights and float activations.
Runs of litert-community Phi-4-mini-instruct on huggingface.co
23.2K
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24-hour runs
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3-day runs
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30-day runs
More Information About Phi-4-mini-instruct huggingface.co Model
Url of Phi-4-mini-instruct
Phi-4-mini-instruct huggingface.co Url
Provider of Phi-4-mini-instruct huggingface.co
Other API from litert-community
Updated:August 31 2026
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