# SmolVLA

Run **SmolVLA** optimized for **Qualcomm Dragonwing IQ9 device's NPU** with [nexaSDK](https://sdk.nexa.ai).

## Quickstart

1. **Install NexaSDK** and create a free account at [sdk.nexa.ai](https://sdk.nexa.ai)
2. **Activate your device** with your access token:

   ```bash
   nexa config set license '<access_token>'
   ```
3. Run the model on Qualcomm NPU in one line:

   ```bash
   nexa infer NexaAI/smolVLA-npu
   ```
   
- Input: Enter input folder path,
- Output: Returns result in npy file, or report error if any required input cannot be found


## Model Description
**SmolVLA** is a lightweight **Vision-Language-Action (VLA)** model built for efficient multimodal understanding and real-time control.  
Developed by the **Hugging Face Smol team**, it unifies **vision**, **language**, and **action** into one coherent model that can perceive, reason, and act — enabling autonomous agents and robotics to run entirely on local hardware.

## Features
- 🧠 **Unified Perception-to-Action** — Combines visual understanding, natural language reasoning, and control generation.  
- ⚡ **Lightweight & Fast** — Designed for real-time inference on laptops, edge boards, and NPUs.  
- 👁️ **Grounded Visual Reasoning** — Links language instructions with specific visual elements and spatial context.  
- 🧩 **Zero-Shot Multimodal Tasks** — Performs visual question answering, task planning, and grounding without retraining.  
- 🔧 **Extensible & Open** — Compatible with robotics frameworks and multimodal datasets for custom fine-tuning.

## Use Cases
- **Embodied AI**: End-to-end perception-action loops for robotics and simulation.  
- **On-Device Agents**: Multimodal assistants that process camera feeds locally.  
- **Autonomous Systems**: Real-time visual reasoning in automotive or IoT devices.  
- **Research**: Alignment studies and grounded reasoning experiments.  
- **Simulation Control**: Vision-driven policy generation for digital twins or VR.  

## Inputs and Outputs
**Input**
- Image(s) or video frames  
- Optional text instruction or query  

**Output**
- Action vector or control command  
- Optional textual reasoning or visual grounding map  

## License
This repo is licensed under the **Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0)** license, which allows use, sharing, and modification only for non-commercial purposes with proper attribution.  
All NPU-related models, runtimes, and code in this project are protected under this non-commercial license and cannot be used in any commercial or revenue-generating applications.  
Commercial licensing or enterprise usage requires a separate agreement.  
For inquiries, please contact `dev@nexa.ai`.