Create README.md

README.md

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