---
library_name: pytorch
license: other
tags:
- bu_auto
- android
pipeline_tag: other

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/statetransformer/web-assets/model_demo.png)

# StateTransformer: Optimized for Qualcomm Devices

StateTransformer is a transformer-based model designed for trajectory prediction in self-driving scenarios. It integrates rasterized map data, agent context, and temporal dynamics to generate accurate future trajectories.

This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/statetransformer) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).

Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.

## Getting Started
There are two ways to deploy this model on your device:

### Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

| Runtime | Precision | Chipset | SDK Versions | Download |
|---|---|---|---|---|
| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.19.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/statetransformer/releases/v0.50.2/statetransformer-tflite-float.zip)

For more device-specific assets and performance metrics, visit **[StateTransformer on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/statetransformer)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/statetransformer) Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for [StateTransformer on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/statetransformer) for usage instructions.

## Model Details

**Model Type:** Model_use_case.driver_assistance

**Model Stats:**
- Model checkpoint: pretrained-mixtral-small
- Input resolution: 1x224x224x58, 1x224x224x58, 1x4x7
- Number of parameters: 90.7M
- Model size (float): 348 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| StateTransformer | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 337.804 ms | 215 - 228 MB | CPU
| StateTransformer | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 461.635 ms | 350 - 368 MB | CPU
| StateTransformer | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 924.988 ms | 442 - 453 MB | CPU
| StateTransformer | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 543.861 ms | 227 - 747 MB | CPU
| StateTransformer | TFLITE | float | Qualcomm® QCS9075 | 786.777 ms | 233 - 654 MB | CPU
| StateTransformer | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 654.374 ms | 357 - 370 MB | CPU
| StateTransformer | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 353.348 ms | 236 - 254 MB | CPU

## License
* The license for the original implementation of StateTransformer can be found
  [here](https://github.com/Tsinghua-MARS-Lab/StateTransformer/blob/main/setup.py).



## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).