Update README.md

README.md

@@ -20,7 +20,7 @@ This is the official implementation of [VideoLLM-online: Online Video Large Lang
 
 - **Cheap and Scalable Streaming Data Synthesis**: Current video datasets for training multimodal LLMs are mostly offline and unsuitable for training an online video language model. Our method **transforms any offline annotation into streaming dialogue data** by prompting open-source LLM. The model is entirely trained on Llama synthesized data.
 
-- **Asynchronous Real-Time Inference**: Our inference method **parallelizes** video encoding, LLM forwarding for video frames, and LLM response generation, arranging them asynchronously. This significantly enhances real-time performance, achieving 10-15 FPS on an A100 GPU.
+- **Parallelized Real-Time Inference**: Our inference method **parallelizes** video encoding, LLM forwarding for video frames, and LLM response generation, arranging them asynchronously. This significantly enhances real-time performance, achieving 10-15 FPS on an A100 GPU.
 
 ### Quick Start
 
@@ -34,7 +34,7 @@ python -m demo.app --resume_from_checkpoint chenjoya/videollm-online-8b-v1plus
 python -m demo.cli --resume_from_checkpoint chenjoya/videollm-online-8b-v1plus
 ```
 
-- (Deprecated, HF Spaces too slow) Try demo at <a href="..." target="_blank"> <img alt="Demo" src="https://img.shields.io/badge/🤗 Hugging Face Spaces-ffc107?color=ffc107" /> </a>
+- (Deprecated, HF Spaces too slow) Try demo at <a href="https://huggingface.co/spaces/chenjoya/videollm-online" target="_blank"> <img alt="Demo" src="https://img.shields.io/badge/🤗 Hugging Face Spaces-ffc107?color=ffc107" /> </a>
 
 By passing ```--resume_from_checkpoint chenjoya/videollm-online-8b-v1plus```, the PEFT checkpoint will be automatically downloaded and applied to ```meta-llama/Meta-Llama-3-8B-Instruct```.