Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese
Authors: name: Khang T. Doan; name: Bao G. Huynh; name: Dung T. Hoang; name: Thuc D. Pham; name: Nhat H. Pham; name: Quan T. M. Nguyen; name: Bang Q. Vo; name: Suong N. Hoang
Published: 2024-08-22 15:15:51

Abstract: 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.