---
license: mit
datasets:
- Vi-VLM/Vista
language:
- vi
library_name: adapter-transformers
pipeline_tag: text-classification
---

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<h1 align="center">MoE-LLaVA-Qwen1.5-1.8B×4-Top2: When Vision meet Small-scaled Language Model and Vietnamese Synthetic Dataset</h1>

<h5 align="center">

# Introducing MoE-LLaVA-Qwen1.5-1.8B×4-Top2 for Vietnamese

We are excited to present MoE-LLaVA-Qwen1.5-1.8B×4-Top2, tailored for the Vietnamese language. This model is part of our ongoing efforts to develop Vision Language Models (VLM) for Vietnamese, a domain that is currently limited and predominantly features larger models (**~7B parameters**). Our model activates approximately **2.2B** 🤗😎 parameters per call, significantly reducing the memory footprint, and it can be quantized for local execution.

## Training Dataset

Our model is trained on the comprehensive [Vi-VLM/Vista dataset](https://huggingface.co./datasets/Vi-VLM/Vista), which includes around 700,000 Vietnamese vision-language samples curated by Gemini Pro. We employed various prompt engineering techniques, including:

- **Few-shot Learning**
- **Caption-based Prompting**
- **Image-based Prompting**

For the COCO dataset, we utilized Llava-style prompts to generate data. For the ShareGPT4V dataset, translation prompts were applied.

### Techniques Used

- **Caption-based Prompting:** Utilizes accurate captions and bounding boxes from the original dataset.
- **Image-based Prompting:** Leverages images to generate captions and conversations.

## Evaluation
- Comming soon 🫡

## Bias, Risks, and Limitations

The dataset may contain biases originating from its sources. Users should remain aware of these potential biases when utilizing the dataset.

## More Information

This dataset represents the first stage of a two-stage development process for a larger model. Stay tuned for future developments by subscribing to our updates.