title: Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model
summary: We introduce Xmodel-VLM, a cutting-edge multimodal vision language model. It
  is designed for efficient deployment on consumer GPU servers. Our work directly
  confronts a pivotal industry issue by grappling with the prohibitive service
  costs that hinder the broad adoption of large-scale multimodal systems. Through
  rigorous training, we have developed a 1B-scale language model from the ground
  up, employing the LLaVA paradigm for modal alignment. The result, which we call
  Xmodel-VLM, is a lightweight yet powerful multimodal vision language model.
  Extensive testing across numerous classic multimodal benchmarks has revealed
  that despite its smaller size and faster execution, Xmodel-VLM delivers
  performance comparable to that of larger models. Our model checkpoints and code
  are publicly available on GitHub at https://github.com/XiaoduoAILab/XmodelVLM.
paper_authors._id: 6645640e7317f54c0de88f74
paper_authors.name: Wanting Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-16T12:37:05.444Z
paper_authors._id: 6526c16310184f8350667bce
paper_authors.avatarUrl: /avatars/afc44d1efe58ce0765bbabbfaf9a1b4b.svg
paper_authors.fullname: Xu
paper_authors.type: user
paper_authors.user: wantingXu
paper_authors._id: 6645640e7317f54c0de88f75
paper_authors.name: Yang Liu
paper_authors._id: 6645640e7317f54c0de88f76
paper_authors.name: Langping He
paper_authors._id: 6645640e7317f54c0de88f77
paper_authors.name: Xucheng Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-16T12:37:27.085Z
paper_authors._id: 637dda5b976326508039b2e9
paper_authors.avatarUrl: /avatars/e5bbc245cef4ff5ca3f9e1ab28bb8b87.svg
paper_authors.fullname: Xucheng Huang
paper_authors.type: user
paper_authors.user: hxc123china
paper_authors._id: 6645640e7317f54c0de88f78
paper_authors.name: Ling Jiang
paper_title: Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model
paper_summary: We introduce Xmodel-VLM, a cutting-edge multimodal vision language model. It
  is designed for efficient deployment on consumer GPU servers. Our work directly
  confronts a pivotal industry issue by grappling with the prohibitive service
  costs that hinder the broad adoption of large-scale multimodal systems. Through
  rigorous training, we have developed a 1B-scale language model from the ground
  up, employing the LLaVA paradigm for modal alignment. The result, which we call
  Xmodel-VLM, is a lightweight yet powerful multimodal vision language model.
  Extensive testing across numerous classic multimodal benchmarks has revealed
  that despite its smaller size and faster execution, Xmodel-VLM delivers
  performance comparable to that of larger models. Our model checkpoints and code
  are publicly available on GitHub at https://github.com/XiaoduoAILab/XmodelVLM.
paper_ai_summary: Xmodel-VLM is a lightweight multimodal vision language model developed for efficient deployment, achieving performance comparable to larger models with a 1B language model trained using the LLaVA paradigm.
paper_ai_keywords: multimodal vision language model consumer GPU servers service costs 1B-scale language model LLaVA paradigm modal alignment multimodal benchmarks