Update README.md

README.md

@@ -7,39 +7,23 @@ inference: false
 
 # This page is still under construction. 
 
-# LLaVA Model Card
+# MoMA Model Card
 
 ## Model details
 
 **Model type:**
-LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
-It is an auto-regressive language model, based on the transformer architecture.
-
-**Model date:**
-LLaVA-v1.5-7B was trained in September 2023.
+MoMA is an open-source image personalization model. It has new attention layers and a multi-modal large language model fine-tuned from LLaVA-7B.
 
 **Paper or resources for more information:**
-https://llava-vl.github.io/
-
-## License
-Llama 2 is licensed under the LLAMA 2 Community License, 
-Copyright (c) Meta Platforms, Inc. All Rights Reserved.
++ Github: https://github.com/KunpengSong/MoMA
++ Paper: https://arxiv.org/abs/2404.05674
 
 **Where to send questions or comments about the model:**
-https://github.com/haotian-liu/LLaVA/issues
+https://github.com/KunpengSong/MoMA
 
 ## Intended use
 **Primary intended uses:**
-The primary use of LLaVA is research on large multimodal models and chatbots.
+The primary use of LLaVA is research on personalized image generation tasks.
 
 **Primary intended users:**
 The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
-
-## Training dataset
-- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
-- 158K GPT-generated multimodal instruction-following data.
-- 450K academic-task-oriented VQA data mixture.
-- 40K ShareGPT data.
-
-## Evaluation dataset
-A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.