--- license: cc-by-nc-4.0 pipeline_tag: image-text-to-text ---

# AutoNeural-VL-1.5B ## **Introduction** **AutoNeural** is an NPU-native vision–language model for in-car assistants, co-designed with a MobileNetV5 encoder and a hybrid Liquid AI 1.2B backbone to deliver **real-time multimodal understanding on Qualcomm SA8295P NPU**. It runs 768×768 images, cuts end-to-end latency by up to **14×**, and improves quantization error by **7×** compared to ViT–Transformer baselines on the same hardware. Key Features: - **NPU-native co-design** – MobileNet-based vision encoder + hybrid Transformer–SSM backbone, built for INT4/8/16 and NPU operator sets. - **Real-time cockpit performance** – Up to **14× lower TTFT**, ~3× faster decode, and 4× longer context (4096 vs 1024) on Qualcomm SA8295P NPU. - **High-resolution multimodal perception** – Supports **768×768** images with ~45 dB SQNR under mixed-precision quantization (W8A16 vision, W4A16 language). - **Automotive-tuned dataset** – Trained with **200k** proprietary cockpit samples (AI Sentinel, Greeter, Car Finder, Safety) plus large-scale Infinity-MM instruction data. - **Production-focused** – Designed for always-on, low-power, privacy-preserving deployment in real vehicles. ## Use Cases AutoNeural powers real-time cockpit intelligence including **in-cabin detection**, **out-cabin awareness**, **HMI understanding**, and **visual + conversational agent**.

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| Metric | InternVL 2B (baseline) | AutoNeural-VL | | :--------------------- | :--------------------: | :-----------: | | TTFT (1× 512² image) | ~1.4 s | **~100 ms** | | Max image size | 448×448 | **768×768** | | SQNR | 28 dB | **45 dB** | | RMS quantization error | 3.98% | **0.562%** | | Decode throughput | ~15 tok/s | **~44 tok/s** | | Context length | 1024 | **4096** | > 📝 These numbers are measured on-device with mixed precision (vision: W8A16; language: W4A16), not simulation. --- # **How to Use** > ⚠️ **Hardware requirement:** AutoNeural is only available 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 '' ``` ### 3) Run the model ```bash nexa infer NexaAI/AutoNeural ``` ### 4) Image input Drag and drop one or more image files into the terminal window. Multiple images can be processed with a single query. --- ## Model architecture

AutoNeural is an NPU-native vision–language model co-designed for integer-only inference on edge devices (e.g. Qualcomm SA8295P). - **Vision encoder.** A MobileNetV5-style CNN initialized from Gemma 3n-E4B, taking 768×768 images and producing a 16×16×2048 feature map. A Multi-Scale Fusion Adapter (MSFA) fuses the last stages and flattens them into **256 visual tokens**, giving strong inductive bias and stable INT8/16 quantization. - **Vision–language connector.** A lightweight 2-layer MLP projects visual tokens into the language embedding space. We deliberately remove normalization from the projector to make activation ranges easier to calibrate for static NPU quantization. - **Language backbone.** A 1.2B-parameter **hybrid Transformer–SSM (“Liquid AI”)** model with 16 layers, interleaving 10 gated-convolution SSM layers with 6 self-attention layers. The SSM layers provide linear-time inference and a compact state instead of a full KV cache, cutting memory I/O while the attention layers preserve strong reasoning and in-context learning. - **Quantization.** The deployed model uses mixed precision (e.g. W8A16 for vision, W4A16 for language) and NPU-aware graph partitioning to meet tight latency and memory budgets without sacrificing accuracy. --- ## Training

AutoNeural follows a four-stage curriculum on large-scale multimodal data plus a proprietary automotive dataset. 1. **Image–text alignment.** Freeze vision and language backbones; train only the projector on image–caption pairs to learn basic visual grounding. 2. **General visual understanding.** Unfreeze the full model and train on broad VQA-style tasks (object/scene understanding, basic reasoning) from the Infinity-MM dataset to build strong general multimodal capability. 3. **Instruction tuning.** Continue training on diverse instruction-following data (documents, charts, OCR, multi-turn dialogue, specialized domains) using a mixture of task weights for balanced performance. 4. **Automotive domain finetuning.** Finetune on ~200k curated cockpit samples (AI Sentinel, Greeter, Car Finder, Safety when getting on/off) plus high-quality synthetic data, with an NPU-aware recipe that combines quantization-aware training, mixed-precision constraints, and calibration to keep post-quantization drift low on real hardware. --- ## **License** This model 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. ## **Enterprise Deployment** For enterprise deployment, custom integrations, or licensing inquiries: 📅 **[Book a Call with Us](https://nexa.ai/book-a-call)**