title: Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese
summary: In this report, we introduce Vintern-1B, a reliable 1-billion-parameters
  multimodal large language model (MLLM) for Vietnamese language tasks. By
  integrating the Qwen2-0.5B-Instruct language model with the
  InternViT-300M-448px visual model, Vintern-1B is optimized for a range of
  applications, including optical character recognition (OCR), document
  extraction, and general question-answering in Vietnamese context. The model is
  fine-tuned on an extensive dataset of over 3 million image-question-answer
  pairs, achieving robust performance and reliable results across multiple
  Vietnamese language benchmarks like OpenViVQA and ViTextVQA. Vintern-1B is
  small enough to fit into various on-device applications easily. Additionally,
  we have open-sourced several Vietnamese vision question answering (VQA)
  datasets for text and diagrams, created with Gemini 1.5 Flash. Our models are
  available at: https://huggingface.co/5CD-AI/Vintern-1B-v2.
paper_authors._id: 66c805887d9956c70e301cc6
paper_authors.name: Khang T. Doan
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paper_authors.fullname: Khang Doan
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paper_authors.user: khang119966
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paper_authors.name: Bao G. Huynh
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paper_authors.fullname: Huynh Gia Bao
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paper_authors.user: baohuynhbk14
paper_authors._id: 66c805887d9956c70e301cc8
paper_authors.name: Dung T. Hoang
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paper_authors.fullname: Suong N Hoang
paper_authors.type: user
paper_authors.user: hoangnhatsuong
paper_authors._id: 66c805887d9956c70e301cc9
paper_authors.name: Thuc D. Pham
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paper_authors.fullname: Dinh-Thuc Pham
paper_authors.type: user
paper_authors.user: ThucPD
paper_authors._id: 66c805887d9956c70e301cca
paper_authors.name: Nhat H. Pham
paper_authors._id: 66c805887d9956c70e301ccb
paper_authors.name: Quan T. M. Nguyen
paper_authors.status: claimed_verified
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paper_authors.fullname: Nguyen Tran Minh Quan
paper_authors.type: user
paper_authors.user: minhquan24
paper_authors._id: 66c805887d9956c70e301ccc
paper_authors.name: Bang Q. Vo
paper_authors.status: claimed_verified
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paper_authors._id: 64ba5951e8762a2751ab04db
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/cdOJmRxXad2A1Qw88KXrT.jpeg
paper_authors.fullname: Võ Quốc Bằng
paper_authors.type: user
paper_authors.user: bavo
paper_authors._id: 66c805887d9956c70e301ccd
paper_authors.name: Suong N. Hoang
paper_title: Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese
paper_summary: In this report, we introduce Vintern-1B, a reliable 1-billion-parameters
  multimodal large language model (MLLM) for Vietnamese language tasks. By
  integrating the Qwen2-0.5B-Instruct language model with the
  InternViT-300M-448px visual model, Vintern-1B is optimized for a range of
  applications, including optical character recognition (OCR), document
  extraction, and general question-answering in Vietnamese context. The model is
  fine-tuned on an extensive dataset of over 3 million image-question-answer
  pairs, achieving robust performance and reliable results across multiple
  Vietnamese language benchmarks like OpenViVQA and ViTextVQA. Vintern-1B is
  small enough to fit into various on-device applications easily. Additionally,
  we have open-sourced several Vietnamese vision question answering (VQA)
  datasets for text and diagrams, created with Gemini 1.5 Flash. Our models are
  available at: https://huggingface.co/5CD-AI/Vintern-1B-v2.
paper_ai_keywords: Vintern-1B multimodal large language model (MLLM) Qwen2-0.5B-Instruct InternViT-300M-448px optical character recognition (OCR) document extraction question-answering image-question-answer pairs OpenViVQA ViTextVQA Vietnamese vision question answering (VQA) Gemini 1.5 Flash