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--- |
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license: mit |
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datasets: |
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- Vi-VLM/Vista |
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language: |
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- vi |
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library_name: adapter-transformers |
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pipeline_tag: text-classification |
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--- |
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# Introducing MoE-LLaVA-Qwen1.5-1.8B×4-Top2 for Vietnamese |
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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. |
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## Training Dataset |
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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: |
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- **Few-shot Learning** |
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- **Caption-based Prompting** |
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- **Image-based Prompting** |
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For the COCO dataset, we utilized Llava-style prompts to generate data. For the ShareGPT4V dataset, translation prompts were applied. |
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### Techniques Used |
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- **Caption-based Prompting:** Utilizes accurate captions and bounding boxes from the original dataset. |
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- **Image-based Prompting:** Leverages images to generate captions and conversations. |
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## Bias, Risks, and Limitations |
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The dataset may contain biases originating from its sources. Users should remain aware of these potential biases when utilizing the dataset. |
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## More Information |
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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. |
