Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese
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.
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Khang T. Doan
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2024-08-26T07:15:26.909Z
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Khang Doan
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Bao G. Huynh
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Huynh Gia Bao
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Dung T. Hoang
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2024-08-23T04:38:18.091Z
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Suong N Hoang
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Thuc D. Pham
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2025-02-14T12:41:17.748Z
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Dinh-Thuc Pham
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ThucPD
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Nhat H. Pham
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Quan T. M. Nguyen
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2024-10-24T07:16:11.090Z
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Nguyen Tran Minh Quan
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minhquan24
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Bang Q. Vo
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2024-08-26T07:15:35.241Z
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Võ Quốc Bằng
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bavo
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Suong N. Hoang
Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese
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.
Vintern-1B, a multimodal large language model, integrates Qwen2 and InternViT for OCR, document extraction, and Vietnamese question-answering by fine-tuning on over 3 million image-question-answer pairs.
multimodal large language model MLLM Qwen2-0.5B-Instruct InternViT-300M-448px optical character recognition document extraction image-question-answer pairs OpenViVQA ViTextVQA Vietnamese vision question answering VQA datasets