File size: 1,402 Bytes
4f45291 b602e5c ced2712 145af7c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
---
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
[](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>
|