litert-community/DeepSeek-R1-Distill-Qwen-1.5B
This model provides a few variants of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B 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.
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 | 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 |
166.50 tk/s |
26.35 tk/s |
6.41 s |
1831.43 MB |
2221 MB |
N/A |
GPU |
dynamic_int8 |
4096 |
927.54 tk/s |
26.98 tk/s |
5.46 s |
1831.43 MB |
2096 MB |
1659 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.