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--- |
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license: mit |
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base_model: |
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B |
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--- |
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# litert-community/DeepSeek-R1-Distill-Qwen-1.5B |
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This model provides a few variants of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) that are ready for deployment on Android using the [LiteRT (fka TFLite) stack](https://ai.google.dev/edge/litert) and [MediaPipe LLM Inference API](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference). |
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## Use the models |
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### Colab |
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*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.* |
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[](https://colab.research.google.com/#fileId=https://huggingface.co/litert-community/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/deepseek_tflite.ipynb) |
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### Android |
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* Download and install [the apk](https://github.com/google-ai-edge/mediapipe-samples/releases/download/v0.1.0/llm_inference_v0.1.0-debug.apk). |
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* Follow the instructions in the app. |
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To build the demo app from source, please follow the [instructions](https://github.com/google-ai-edge/mediapipe-samples/blob/main/examples/llm_inference/android/README.md) from the GitHub repository. |
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## Performance |
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### Android |
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Note that all benchmark stats are from a Samsung S24 Ultra with 1280 KV cache size, 512 tokens prefill, 128 tokens decode. |
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<table border="1"> |
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<tr> |
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<th></th> |
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<th>Backend</th> |
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<th>Prefill (tokens/sec)</th> |
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<th>Decode (tokens/sec)</th> |
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<th>Time-to-first-token (sec)</th> |
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<th>Memory (RSS in MB)</th> |
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<th>Model size (MB)</th> |
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</tr> |
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<tr> |
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<td>fp32 (baseline)</td> |
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<td rowspan="2">CPU</td> |
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<td><p style="text-align: right">45</p></td> |
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<td><p style="text-align: right">6</p></td> |
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<td><p style="text-align: right">8</p></td> |
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<td><p style="text-align: right">6,213</p></td> |
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<td><p style="text-align: right">7,124</p></td> |
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</tr> |
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<tr> |
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<td>dynamic_int8</td> |
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<td><p style="text-align: right">261</p></td> |
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<td><p style="text-align: right">23</p></td> |
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<td><p style="text-align: right">2 </p></td> |
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<td><p style="text-align: right">1,936 </p></td> |
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<td><p style="text-align: right">1,861</p></td> |
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</tr> |
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</table> |
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* Model Size: measured by the size of the .tflite flatbuffer (serialization format for LiteRT models) |
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* Memory: indicator of peak RAM usage |
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* The inference on CPU is accelerated via the LiteRT [XNNPACK](https://github.com/google/XNNPACK) delegate with 4 threads |
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* Benchmark is done assuming XNNPACK cache is enabled |
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* dynamic_int8: quantized model with int8 weights and float activations. |
