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litert-community/
Qwen2.5-0.5B-Instruct

Name: Qwen2.5-0.5B-Instruct
Availability: InStock

tflite

chat

text-generation

Qwen/Qwen2.5-0.5B-Instruct

finetune

apache-2.0

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litert-community/Qwen2.5-0.5B-Instruct

This model provides a few variants of Qwen/Qwen2.5-0.5B-Instruct that are ready for deployment on Android using the LiteRT (fka TFLite) 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.

Open In Colab

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 with multiple prefill signatures enabled.

	Backend	Prefill (tokens/sec)	Decode (tokens/sec)	Time-to-first-token (sec)	Memory (RSS in MB)	Model size (MB)
fp32 (baseline)	cpu	90.30 tk/s	16.71 tk/s	5.24 s	4,503 MB	1,898 MB
dynamic_int8	cpu	250.73 tk/s	29.97 tk/s	2.31 s	1,363 MB	521 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
dynamic_int8: quantized model with int8 weights and float activations.