Update README.md

README.md

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-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+<p align="center">
+  <div style="display: flex;text-align: center;">
+    <div>
+      <img src="https://firebasestorage.googleapis.com/v0/b/database-7ca5c.appspot.com/o/llm%2F68747470733a2f2f7331312e617831782e636f6d2f323032332f31322f32382f70697176444d562e706e67.png?alt=media&token=30a2470d-861e-4295-a7f4-da48231724cf" width="250" style="margin-bottom: 0.2;"/>
+    </div>
+    <div>
+      <img src="https://firebasestorage.googleapis.com/v0/b/database-7ca5c.appspot.com/o/llm%2Flogo_qwen.jpg?alt=media&token=fd2cd557-2f45-4f94-86d3-a5e7c9eef630" width="600" style="margin-bottom: 1rem;"/>
+    </div>
+  </div>
+<p>
+<h1 align="center">MoE-LLaVA-Qwen1.5-1.8B×4-Top2: When Vision meet Small-scaled Language Model and Vietnamese Synthetic Dataset</h1>
 
-# ft-moe-llava-qwen1.5-1.8b-vista-1ep
+<h5 align="center">
 
-This model was trained from scratch on an unknown dataset.
+# Introducing MoE-LLaVA-Qwen1.5-1.8B×4-Top2 for Vietnamese
 
-## Model description
+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.
 
-More information needed
+## Bias, Risks, and Limitations
 
-## Intended uses & limitations
+The dataset may contain biases originating from its sources. Users should remain aware of these potential biases when utilizing the dataset.
 
-More information needed
+## 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.
 
 ## Training and evaluation data
 
-More information needed
+### 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**
+
+### Techniques Used
+
+- **MoE-LLaVA**: [MoE-LLaVA](https://github.com/PKU-YuanGroup/MoE-LLaVA/tree/main)
+
+## Evaluation
+- Comming soon 🫡
 
 ## Training procedure