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 # vLLM-omni: Multi-modal Extension for vLLM
 
 vLLM-omni is designed to extend vLLM capabilities to support multi-modality model inference and serving, particularly focusing on non-autoregressive structures and non-textual outputs.
+
+## 🎯 Overview
+
+Traditional vLLM systems are limited to text-based, autoregressive generation. vLLM-omni addresses this limitation by enabling support for:
+
+- **Multi-modal Models**: Text, image, video, audio, and sensor data processing
+- **Non-autoregressive Architectures**: Diffusion Transformers (DiT) and other parallel generation models
+- **Heterogeneous Outputs**: Beyond traditional text generation to structured, binary, and streaming outputs
+
+## 🏗️ Architecture
+
+vLLM-omni is built on a modular architecture that extends vLLM's core functionality:
+
+
+## 🚀 Key Features
+
+### Multi-Engine Support
+
+- **Autoregressive Engine**: Traditional text generation with enhanced KV-caching
+- **Diffusion Engine**: Support for DiT models and iterative generation
+- **Hybrid Engine**: Combined AR+DiT processing pipelines
+
+### Modality Processing
+
+- **Text**: Advanced tokenization and embedding generation
+- **Image**: Vision encoder integration (CLIP, etc.)
+- **Audio**: Speech processing and audio embedding
+- **Video**: Frame-by-frame and temporal processing
+- **Sensor**: IoT and sensor data interpretation
+
+### Output Formats
+
+- **Structured Data**: JSON, XML, and custom formats
+- **Binary Outputs**: Images, audio, and video generation
+- **Streaming**: Real-time progressive generation
+- **Multipart**: Combined multi-modal responses
+
+## 📋 Supported Models
+
+### AR + Diffusion Transformer (DiT) Models
+- Qwen-Image (Image generation and editing)
+- Qwen-omni (Thinker-Talker-Codec structure)
+- Custom DiT and hiybrid architectures
+
+## 🛠️ Installation
+
+### Prerequisites
+
+- Python 3.8+
+- PyTorch 2.0+
+- CUDA 11.8+ (for GPU acceleration)
+
+### Install from Source
+
+```bash
+git clone https://github.com/your-org/vllm-omni.git
+pip install -r requirements.txt
+cd vllm-omni
+pip install -e .
+```