Total runs:
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Growth Rate:
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Sponsored by
APIMart
litert-community / Qwen2.5-1.5B-Instruct
Model's Last Updated: November 26 2025
text-generation
Introduction of Qwen2.5-1.5B-Instruct
Model Details of Qwen2.5-1.5B-Instruct
litert-community/Qwen2.5-1.5B-Instruct
This model provides a few variants of Qwen/Qwen2.5-1.5B-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.
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.
iOS
- Clone the MediaPipe samples repository and follow the instructions to build the LLM Inference iOS Sample App using XCode.
- Run the app via the iOS simulator or deploy to an iOS device.
Performance
Android
Note that all benchmark stats are from a Samsung S24 Ultra and multiple prefill signatures enabled.
| Backend | Quantization scheme | Context length | Prefill (tokens/sec) | Decode (tokens/sec) | Time-to-first-token (sec) | CPU Memory (RSS in MB) | GPU Memory (RSS in MB) | Model size (MB) | |
|---|---|---|---|---|---|---|---|---|---|
|
CPU |
fp32 (baseline) |
1280 |
27 tk/s |
6 tk/s |
9.88 s |
6,144 MB |
|
5,895 MB |
|
|
dynamic_int8 |
1280 |
106 tk/s |
23 tk/s |
2.74 s |
1,820 MB |
|
1,523 MB |
||
|
4096 |
63 tk/s |
20 tk/s |
4.40 s |
2,042 MB |
|
1,523 MB |
|||
|
GPU |
1280 |
706 tk/s |
24 tk/s |
6.94 s |
3,175 MB |
1,504 MB |
1,523 MB |
||
|
4096 |
417 tk/s |
22 tk/s |
7.93 s |
3,176 MB |
1,875 MB |
1,523 MB |
- For the list of supported quantization schemes see supported-schemes . For these models, we are using prefill signature lengths of 32, 128, 512 and 1280.
- 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 run with cache enabled and initialized. During the first run, the time to first token may differ.
Runs of litert-community Qwen2.5-1.5B-Instruct on huggingface.co
82.5K
Total runs
0
24-hour runs
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3-day runs
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7-day runs
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30-day runs
More Information About Qwen2.5-1.5B-Instruct huggingface.co Model
Url of Qwen2.5-1.5B-Instruct
Qwen2.5-1.5B-Instruct huggingface.co Url
Provider of Qwen2.5-1.5B-Instruct huggingface.co
Other API from litert-community
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