Update README.md

README.md

@@ -9,11 +9,11 @@ tags:
 
 ![logo.jpg](logo.jpg)
 
-[Ivy\-VL] is a lightweight multimodal model with only 3B parameters. It accepts both image and text inputs to generate text outputs. 
+`Ivy-VL` is a lightweight multimodal model with only 3B parameters. It accepts both image and text inputs to generate text outputs. 
 
-Thanks to its lightweight design, it can be deployed on edge devices such as AI glasses and smartphones, offering low memory usage and high speed while maintaining strong performance on multimodal tasks. Some well-known small models include PaliGemma 3B, Moondream2, Qwen2VL, InternVL2, and InternVL2.5. Ivy-VL outperforms them on multiple benchmarks.
+Thanks to its lightweight design, it can be deployed on edge devices such as AI glasses and smartphones, offering low memory usage and high speed while maintaining strong performance on multimodal tasks. Some well-known small models include [PaliGemma 3B](https://huggingface.co/google/paligemma-3b-mix-448), [Moondream2](https://huggingface.co/vikhyatk/moondream2), [Qwen2-VL-2B](https://huggingface.co/Qwen/Qwen2-VL-2B), [InternVL2-2B](https://huggingface.co/OpenGVLab/InternVL2-2B), and [InternVL2_5-2B](https://huggingface.co/OpenGVLab/InternVL2_5-2B). Ivy-VL outperforms them on multiple benchmarks.
 
-The model is built upon the [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) language model, with [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) serving as the vision encoder. 
+The model is built upon the [`Qwen/Qwen2.5-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) language model, with [`google/siglip-so400m-patch14-384`](https://huggingface.co/google/siglip-so400m-patch14-384) serving as the vision encoder. 
 
 # Model Summary:
 
@@ -50,7 +50,7 @@ import warnings
 
 warnings.filterwarnings("ignore")
 
-pretrained = "AI-Safeguard/Ivy-VL"
+pretrained = "AI-Safeguard/Ivy-VL-llava"
 
 model_name = "llava_qwen"
 device = "cuda"
@@ -97,16 +97,18 @@ print(text_outputs)
 # Future Plan:
 
 *   We plan to release more versions of LLMs in different sizes.
-    
+
 *   We will focus on improving the performance of the video modality.
     
 
 # Citation:
 
+If you find our work helpful, feel free to give us a cite.
+
 ```plaintext
 @misc{ivy2024ivy-vl,
     title={Ivy-VL:Compact Vision-Language Models Achieving SOTA with Optimal Data},
-    url={https://huggingface.co/AI-Safeguard/Ivy-VL},
+    url={https://huggingface.co/AI-Safeguard/Ivy-VL-llava},
     author={Ivy Zhang,Jenny N,Theresa Yu and David Qiu},
     month={December},
     year={2024}