mlsanityData to 7 Oct 2026

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

arXiv 2408.12480 ↗First posted 22 Aug 2024cs.LG cs.CLcited by 12

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 15 of its 20 references that are in the map. First posted on arXiv 22 Aug 2024.

Builds on

15 of 20 references.

Closest ancestors · 6

Same direction, newest first

  1. 2024MiniCPM-V: A GPT-4V Level MLLM on Your Phone3 Aug
  2. 2024LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models10 Jul
  3. 2024LaVy: Vietnamese Multimodal Large Language Model11 Apr
  4. 2024MoE-LLaVA: Mixture of Experts for Large Vision-Language Models29 Jan
  5. 2023InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks21 Dec
  6. 2023Improved Baselines with Visual Instruction Tuning5 Oct

Other references · 9

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2023Visual Instruction Tuningcited by 7,792
  4. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,175
  5. 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,179
  6. 2024Qwen2 Technical Reportcited by 2,076
  7. 2023OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamesecited by 20
  8. 2024ViTextVQA: A Large-Scale Visual Question Answering Dataset and a Novel Multimodal Feature Fusion Method for Vietnamese Text Comprehension in Imagescited by 7
  9. 2022UIT-HWDB: Using Transferring Method to Construct A Novel Benchmark for Evaluating Unconstrained Handwriting Image Recognition in Vietnamesecited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 5.

12 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025VMMU: A Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark
  2. 2023ViCLEVR: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in Vietnamese
  3. 2024CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
  4. 2023TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones
  5. 2023What Large Language Models Bring to Text-rich VQA?
  6. 2024NVLM: Open Frontier-Class Multimodal LLMs
  7. 2025BcQLM: Efficient Vision-Language Understanding with Distilled Q-Gated Cross-Modal Fusion
  8. 2025Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models