title: MobileVLM V2: Faster and Stronger Baseline for Vision Language Model
summary: We introduce MobileVLM V2, a family of significantly improved vision language
  models upon MobileVLM, which proves that a delicate orchestration of novel
  architectural design, an improved training scheme tailored for mobile VLMs, and
  rich high-quality dataset curation can substantially benefit VLMs' performance.
  Specifically, MobileVLM V2 1.7B achieves better or on-par performance on
  standard VLM benchmarks compared with much larger VLMs at the 3B scale.
  Notably, our 3B model outperforms a large variety of VLMs at the 7B+ scale. Our
  models will be released at https://github.com/Meituan-AutoML/MobileVLM .
paper_authors._id: 65c2f3261eeff9b8dba8d8b5
paper_authors.name: Xiangxiang Chu
paper_authors._id: 65c2f3261eeff9b8dba8d8b6
paper_authors.name: Limeng Qiao
paper_authors._id: 65c2f3261eeff9b8dba8d8b7
paper_authors.name: Xinyu Zhang
paper_authors._id: 65c2f3261eeff9b8dba8d8b8
paper_authors.name: Shuang Xu
paper_authors._id: 65c2f3261eeff9b8dba8d8b9
paper_authors.name: Fei Wei
paper_authors._id: 65c2f3261eeff9b8dba8d8ba
paper_authors.name: Yang Yang
paper_authors._id: 65c2f3261eeff9b8dba8d8bb
paper_authors.name: Xiaofei Sun
paper_authors._id: 65c2f3261eeff9b8dba8d8bc
paper_authors.name: Yiming Hu
paper_authors._id: 65c2f3261eeff9b8dba8d8bd
paper_authors.name: Xinyang Lin
paper_authors._id: 65c2f3261eeff9b8dba8d8be
paper_authors.name: Bo Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-05T08:49:33.528Z
paper_authors._id: 64688d93dcbb937d56b4fbae
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64688d93dcbb937d56b4fbae/AHf01DkvhL8p_E9ldwggW.jpeg
paper_authors.fullname: Bo Zhang
paper_authors.type: user
paper_authors.user: boydcheung
paper_authors._id: 65c2f3261eeff9b8dba8d8bf
paper_authors.name: Chunhua Shen
paper_title: MobileVLM V2: Faster and Stronger Baseline for Vision Language Model
paper_summary: We introduce MobileVLM V2, a family of significantly improved vision language
  models upon MobileVLM, which proves that a delicate orchestration of novel
  architectural design, an improved training scheme tailored for mobile VLMs, and
  rich high-quality dataset curation can substantially benefit VLMs' performance.
  Specifically, MobileVLM V2 1.7B achieves better or on-par performance on
  standard VLM benchmarks compared with much larger VLMs at the 3B scale.
  Notably, our 3B model outperforms a large variety of VLMs at the 7B+ scale. Our
  models will be released at https://github.com/Meituan-AutoML/MobileVLM .
paper_ai_summary: MobileVLM V2 enhances vision language models through improved architecture, training, and dataset curation, achieving performance on par with or better than larger models.
paper_ai_keywords: vision language models architectural design training scheme dataset curation VLM benchmarks