---
title: Multimodal Gemma-270M Demo
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.46.1
app_file: app.py
pinned: false
license: mit
---

# Multimodal Gemma-270M Demo

A live demo of the multimodal vision-language model based on Google's Gemma-270M, trained using the LLaVA architecture.

## Model Info

- **Base Model**: Google Gemma-270M (270 million parameters)
- **Vision Encoder**: CLIP ViT-Large/14@336px
- **Architecture**: LLaVA-style vision-language fusion
- **Training**: 7 epochs on LLaVA-150K dataset
- **Trainable Parameters**: 18.6M / 539M total

## Features

- 🖼️ **Image Understanding**: Upload any image and ask questions about it
- 💬 **Conversational AI**: Natural language responses about visual content
- 🎯 **Instruction Following**: Follows specific questions and prompts
- ⚙️ **Adjustable Parameters**: Control response length and creativity

## Usage

1. **Load Model**: Click "🚀 Load Model" to download and initialize the model
2. **Upload Image**: Use the image upload area to select your image
3. **Ask Questions**: Type your question in the text box
4. **Get Response**: The model will analyze the image and provide a response

## Example Questions

- "What do you see in this image?"
- "Describe the main objects in the picture"
- "What colors are prominent in this image?"
- "Are there any people in the image?"
- "What's the setting or location?"

## Technical Details

The model uses:
- **Vision Processing**: CLIP for image encoding
- **Language Generation**: Gemma-270M with LoRA fine-tuning
- **Multimodal Fusion**: Trainable projection layer
- **Quantization**: 4-bit for efficient inference

## Links

- **Model Repository**: [sagar007/multimodal-gemma-270m-llava](https://huggingface.co/sagar007/multimodal-gemma-270m-llava)
- **Source Code**: [GitHub](https://github.com/sagar431/multimodal-gemma-270m)

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