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Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese

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arxiv 2408.12480 v2 pith:TMS64CPH submitted 2024-08-22 cs.LG cs.CL

Khang T. Doan , Bao G. Huynh , Dung T. Hoang , Thuc D. Pham , Nhat H. Pham , Quan T.M. Nguyen , Bang Q. Vo , Suong N. Hoang This is my paper
classification cs.LGcs.CL
keywords languagemodelvietnamesevintern-1bapplicationslargemultimodalreliable
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AutoJudger: An Agent-Driven Framework for Efficient Benchmarking of MLLMs

    cs.CL 2025-05 conditional novelty 6.0 of 10

    An agent-driven framework adaptively selects a small subset of benchmark questions for MLLMs, preserving over 90% ranking accuracy with roughly 4-5% of the data.

  2. A Survey on Vietnamese Document Analysis and Recognition: Challenges and Future Directions

    cs.CV 2025-06 conditional novelty 3.0 of 10

    A survey of Vietnamese document analysis and recognition that catalogs methods, datasets, and open challenges without introducing new experimental results.

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