Update README.md

README.md

@@ -1,66 +1,27 @@
-# **Overview**
+# AutoNeural-VL-1.5B
+
+## **Introduction**
 
 **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 **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.
+AutoNeural powers real-time cockpit intelligence including **in-cabin safety**, **out-of-cabin awareness**, **HMI understanding**, and **visual + conversational function calls**.
 
 ---
 
-### 🚗 **Automotive-Grade Multimodal Intelligence**
+## Use Cases
 
-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**
+## ⚡ **Benchmarks on NPU**
 
 Validated on **Qualcomm SA8295P NPU**:
 
@@ -107,6 +68,53 @@ Multiple images can be processed with a single query.
 
 ---
 
+## **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.
+
+---
+
 # **License**
 
 The AutoNeural model is released under the **Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0)** license.