Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese
<|reference_start|>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.<|reference_end|>
@article{doan2024vintern-1b:,
    title={Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese},
    author={Khang T. Doan, Bao G. Huynh, Dung T. Hoang, Thuc D. Pham, Nhat H.
  Pham, Quan T.M. Nguyen, Bang Q. Vo and Suong N. Hoang},
    journal={arXiv preprint arXiv:2408.12480},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12480},
    primaryClass={cs.LG cs.CL}
}