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README.md

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+# **Overview**
+
+**AutoNeural** is a next-generation, **NPU-native multimodal vision–language model** co-designed from the ground up for real-time, on-device inference. Instead of adapting GPU-first architectures, AutoNeural redesigns both **vision encoding** and **language modeling** for the constraints and capabilities of NPUs—achieving **14× faster latency**, **7× lower quantization error**, and **real-time automotive performance** even under aggressive low-precision settings.
+
+AutoNeural integrates:
+
+* A **MobileNetV5-based vision encoder** with depthwise separable convolutions.
+* A **Liquid AI hybrid Transformer-SSM language backbone** that dramatically reduces KV-cache overhead.
+* A **normalization-free MLP connector** tailored for quantization stability.
+* Mixed-precision **W8A16 (vision)** and **W4A16 (language)** inference validated on real Qualcomm NPUs.
+
+AutoNeural powers real-time cockpit intelligence including **in-cabin safety**, **out-of-cabin awareness**, **HMI understanding**, and **visual + conversational function calls**, as demonstrated in the on-device results (Page 6 figure) .
+
+---
+
+# **Key Features**
+
+### 🔍 **MobileNetV5 Vision Encoder (300M)**
+
+Optimized for edge hardware, with:
+
+* **Depthwise separable convolutions** for low compute and bounded activations.
+* **Local attention bottlenecks** only in late stages for efficient long-range reasoning.
+* **Multi-Scale Fusion Adapter (MSFA)** producing a compact **16×16×2048** feature map.
+* Stable **INT8/16** behavior with minimal post-quantization degradation.
+
+Yields **5.8× – 14× speedups** over ViT baselines across 256–768 px inputs.
+
+---
+
+### 🧠 **Hybrid Transformer-SSM Language Backbone (1.2B)**
+
+Designed for NPU memory hierarchies:
+
+* **5:1 ratio of SSM layers to Transformer attention layers**
+* **Linear-time gated convolution layers** for most steps
+* **Tiny rolling state** instead of KV-cache → up to **60% lower memory bandwidth**
+* **W4A16 stable quantization** across layers
+
+---
+
+### 🔗 **Normalization-Free Vision–Language Connector**
+
+A compact 2-layer MLP using **SiLU**, deliberately **removing RMSNorm** to avoid unstable activation ranges during static quantization.
+
+Ensures reliable deployment on W8A16/W4A16 pipelines.
+
+---
+
+### 🚗 **Automotive-Grade Multimodal Intelligence**
+
+Trained on **10M Infinity-MM samples** plus **200k automotive cockpit samples**, covering:
+
+* AI Sentinel (vehicle security)
+* AI Greeter (identity recognition)
+* Car Finder (parking localization)
+* Passenger safety monitoring
+
+Ensures robust performance across lighting, demographics, weather, and motion scenarios.
+
+---
+
+### ⚡ **Real NPU Benchmarks**
+
+Validated on **Qualcomm SA8295P NPU**:
+
+| Metric                    | Baseline (InternVL 2B) | **AutoNeural-VL** |
+| ------------------------- | ---------------------- | ----------------- |
+| **TTFT**                  | ~1.4 s                 | **~100 ms**       |
+| **Max Vision Resolution** | 448×448                | **768×768**       |
+| **RMS Quant Error**       | 3.98%                  | **0.56%**         |
+| **Decode Throughput**     | 15 tok/s               | **44 tok/s**      |
+| **Context Length**        | 1024                   | **4096**          |
+
+---
+
+# **How to Use**
+
+> ⚠️ **Hardware requirement:** AutoNeural is optimized for **Qualcomm NPUs**.
+
+### 1) Install Nexa-SDK
+
+Download the SDK，follow the installation steps provided on the model page.
+
+---
+
+### 2) Configure authentication
+
+Create an access token in the Model Hub, then run:
+
+```bash
+nexa config set license '<access_token>'
+```
+
+---
+
+### 3) Run the model
+
+```bash
+nexa infer NexaAI/AutoNeural
+```
+
+### Image input
+
+Drag and drop one or more image files into the terminal window.
+Multiple images can be processed with a single query.
+
+### Example prompts
+
+* “Is there any safety risk for the child in this image?”
+* “Explain the meaning of this warning light.”
+* “What are the parking rules shown in this sign?”
+* “Create a calendar event based on this poster.”
+
+---
+
+# **License**
+
+The AutoNeural model is released under the **Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0)** license.
+
+You may:
+
+* Use the model for **non-commercial** purposes
+* Modify and redistribute it with attribution
+
+For **commercial licensing**, please contact:
+**[dev@nexa.ai](mailto:dev@nexa.ai)**