Update README.md

README.md

@@ -1,3 +1,45 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+library_name: transformers
+tags:
+- vision-language-model
+- image-text-to-text
+- linear-attention
+- gated-deltanet
+- infinitevl
+- multimodal
+base_model: Qwen/Qwen2.5-VL-3B-Instruct
+pipeline_tag: image-text-to-text
+---
+
+<div align="center">
+
+# InfiniteVL: Synergizing Linear and Sparse Attention for Highly-Efficient, Unlimited-Input VLMs
+
+<a href="https://arxiv.org/abs/YOUR_ARXIV_ID"><img src="https://img.shields.io/badge/Paper-ArXiv-b31b1b.svg" alt="Paper"></a>
+<a href="https://github.com/YOUR_USERNAME/InfiniteVL"><img src="https://img.shields.io/badge/GitHub-Code-black" alt="Code"></a>
+<a href="LICENSE"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License"></a>
+
+</div>
+
+## 📖 Introduction
+
+**InfiniteVL** is a linear-complexity Vision-Language Model (VLM) developed by **Huazhong University of Science and Technology (HUST)** and **Horizon Robotics**.
+
+Traditional Transformer-based VLMs suffer from quadratic computational complexity ($O(N^2)$) and growing KV-cache memory usage. **InfiniteVL** solves this by synergizing **Sliding Window Attention (SWA)** with **Gated DeltaNet**, enabling **unlimited input tokens** and **real-time streaming**.
+
+### Key Features
+*   **🚀 Linear Complexity ($O(N)$):** Reduces per-token latency by **3.6×** compared to Qwen2.5-VL-3B.
+*   **📉 Constant Memory:** Maintains a fixed GPU memory usage (~9GB) regardless of sequence length.
+*   **⚡ Real-Time Streaming:** Sustains a stable **24 FPS** throughput for long video understanding on a single RTX 4090.
+*   **🧠 Hybrid Architecture:** 75% Gated DeltaNet (Global Context) + 25% SWA (Local Detail).
+
+![Performance Comparison](teaser.png)
+
+## 🛠️ Requirements
+
+To use InfiniteVL, you need to install the linear attention kernels.
+
+```bash
+pip install transformers torch
+pip install fla  # Flash Linear Attention