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---
title: MiniCPM-V-4.5 Multimodal Chat
emoji: 🚀
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.44.0
app_file: app.py
pinned: false
license: apache-2.0
---
# MiniCPM-V-4.5 Multimodal Chat 🚀
A powerful Gradio interface for the MiniCPM-V-4.5 multimodal model - a GPT-4V level MLLM with only 8B parameters!
## Features
- 📸 **Image Understanding**: Analyze single or multiple images with high-resolution support (up to 1.8M pixels)
- 🎥 **Video Understanding**: Process videos with high refresh rate (up to 10 FPS) and efficient compression
- 📄 **Document Parsing**: Strong OCR capabilities and PDF document parsing
- 🧠 **Thinking Modes**: Choose between fast thinking for efficiency or deep thinking for complex problems
- 🌍 **Multilingual**: Support for 30+ languages
- ⚙️ **Customizable**: Adjust FPS, context size, temperature, and system prompts
## Model Capabilities
MiniCPM-V-4.5 achieves state-of-the-art performance across multiple benchmarks:
- Surpasses GPT-4o-latest and Gemini-2.0 Pro on vision-language tasks
- Leading OCR performance on OCRBench
- Efficient video token compression (96x rate)
- Trustworthy behaviors with multilingual support
## Usage
1. **Upload**: Choose an image or video file
2. **Configure**: Adjust settings like FPS (for videos), context size, and temperature
3. **Prompt**: Enter your question or use the system prompt for specific instructions
4. **Generate**: Click the generate button to get the model's response
## Examples
- "What objects do you see in this image?"
- "Describe the main action happening in this video"
- "Read and transcribe any text visible in the image"
- "Analyze this image from an artistic perspective"
## Technical Details
- **Architecture**: Built on Qwen3-8B and SigLIP2-400M
- **Parameters**: 8B total parameters
- **Video Processing**: 3D-Resampler with temporal understanding
- **Resolution**: Supports images up to 1344x1344 pixels
- **Efficiency**: 4x fewer visual tokens than most MLLMs
## License
This model is released under the MiniCPM Model License. Free for academic research and commercial use after registration.
## Citation
```bibtex
@article{yao2024minicpm,
title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone},
author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others},
journal={Nat Commun 16, 5509 (2025)},
year={2025}
}
``` |