README.md

---
license: mit
tags:
- LiteRT
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
---

# litert-community/DeepSeek-R1-Distill-Qwen-1.5B

This model was converted to LiteRT (aka TFLite) format from [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) using [Google AI Edge Torch](https://github.com/google-ai-edge/ai-edge-torch).

## Run the model in colab

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/#fileId=https://huggingface.co/litert-community/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/deepseek%20tflite.ipynb)

## Run the model on Android

Please follow the [instructions](https://github.com/google-ai-edge/mediapipe-samples/blob/main/examples/llm_inference/android/README.md).

## Benchmarking results

Note that all benchmark stats are from a Samsung S24 Ultra.

<table border="1">
  <tr>
    <th>Model</th>
      <td colspan="2">DeepSeek-R1-Distill-Qwen-1.5B (Int8 quantized)</td>
  </tr>
  <tr>
    <th>Params</th>
    <td colspan="2">1.78 B</td>
  </tr>
  <tr>
    <th></th>
    <td><b>Prefill 512 tokens</b></td><td><b>Decode 128 tokens</b></td>
  </tr>
  <tr>
    <th>LiteRT tk/s (XNNPACK, 4 threads)</th>
    <td>260.95</td><td>23.126</td>
  </tr>
  <tr>
    <th>GGML tk/s (CPU, 4 threads)</th>
      <td>64.66</td><td>23.85</td>
  </tr>
</table>