title: ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced
    MiniGPT-4
summary: In recent years, large language models (LLMs) have made significant progress
  in natural language processing (NLP), with models like ChatGPT and GPT-4
  achieving impressive capabilities in various linguistic tasks. However,
  training models on such a large scale is challenging, and finding datasets that
  match the model's scale is often difficult. Fine-tuning and training models
  with fewer parameters using novel methods have emerged as promising approaches
  to overcome these challenges. One such model is MiniGPT-4, which achieves
  comparable vision-language understanding to GPT-4 by leveraging novel
  pre-training models and innovative training strategies. However, the model
  still faces some challenges in image understanding, particularly in artistic
  pictures. A novel multimodal model called ArtGPT-4 has been proposed to address
  these limitations. ArtGPT-4 was trained on image-text pairs using a Tesla A100
  device in just 2 hours, using only about 200 GB of data. The model can depict
  images with an artistic flair and generate visual code, including aesthetically
  pleasing HTML/CSS web pages. Furthermore, the article proposes novel benchmarks
  for evaluating the performance of vision-language models. In the subsequent
  evaluation methods, ArtGPT-4 scored more than 1 point higher than the current
  state-of-the-art model and was only 0.25 points lower than artists on
  a 6-point scale. Our code and pre-trained model are available at
  https://huggingface.co/Tyrannosaurus/ArtGPT-4.
paper_authors._id: 6461879a933afb0106ab7fef
paper_authors.name: Zhengqing Yuan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T12:54:49.864Z
paper_authors._id: 62f3cc40b3e6016d46027e1d
paper_authors.avatarUrl: /avatars/9b26d2fd5e8574f633ffd97af1700b45.svg
paper_authors.fullname: YuanZhengqing
paper_authors.type: user
paper_authors.user: YuanZhengqing
paper_authors._id: 6461879a933afb0106ab7ff0
paper_authors.name: Huiwen Xue
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T12:55:18.865Z
paper_authors._id: 63ef336ed0f7b3b85ac10cbc
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1677037921739-63ef336ed0f7b3b85ac10cbc.jpeg
paper_authors.fullname: xuehuiwen
paper_authors.type: user
paper_authors.user: SmellStone
paper_authors._id: 6461879a933afb0106ab7ff1
paper_authors.name: Xinyi Wang
paper_authors._id: 6461879a933afb0106ab7ff2
paper_authors.name: Yongming Liu
paper_authors._id: 6461879a933afb0106ab7ff3
paper_authors.name: Zhuanzhe Zhao
paper_authors._id: 6461879a933afb0106ab7ff4
paper_authors.name: Kun Wang
paper_title: ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced
    MiniGPT-4
paper_summary: In recent years, large language models (LLMs) have made significant progress
  in natural language processing (NLP), with models like ChatGPT and GPT-4
  achieving impressive capabilities in various linguistic tasks. However,
  training models on such a large scale is challenging, and finding datasets that
  match the model's scale is often difficult. Fine-tuning and training models
  with fewer parameters using novel methods have emerged as promising approaches
  to overcome these challenges. One such model is MiniGPT-4, which achieves
  comparable vision-language understanding to GPT-4 by leveraging novel
  pre-training models and innovative training strategies. However, the model
  still faces some challenges in image understanding, particularly in artistic
  pictures. A novel multimodal model called ArtGPT-4 has been proposed to address
  these limitations. ArtGPT-4 was trained on image-text pairs using a Tesla A100
  device in just 2 hours, using only about 200 GB of data. The model can depict
  images with an artistic flair and generate visual code, including aesthetically
  pleasing HTML/CSS web pages. Furthermore, the article proposes novel benchmarks
  for evaluating the performance of vision-language models. In the subsequent
  evaluation methods, ArtGPT-4 scored more than 1 point higher than the current
  state-of-the-art model and was only 0.25 points lower than artists on
  a 6-point scale. Our code and pre-trained model are available at
  https://huggingface.co/Tyrannosaurus/ArtGPT-4.
paper_ai_keywords: large language models (LLMs) natural language processing (NLP) ChatGPT GPT-4 fine-tuning parameter-efficient fine-tuning MiniGPT-4 vision-language understanding pre-training models multimodal model ArtGPT-4 image-text pairs Tesla A100 visual code HTML/CSS web pages benchmarks vision-language models state-of-the-art artists