---
license: apache-2.0
base_model:
- mistralai/Pixtral-12B-2409
library_name: transformers
---
# Pixtral-12B Vision Encoder

## Model Overview
This repository provides direct access to the vision encoder module extracted from the Pixtral-12B multimodal model. By isolating the vision encoder, we enable researchers and developers to leverage the powerful visual feature extraction capabilities for downstream vision tasks.

## Key Features
- **Standalone Vision Encoder**: Extracted from the full Pixtral-12B model
- **Lightweight Architecture**: Optimized 400M parameter vision encoder
- **Flexible Usage**: Easily integrated into various computer vision pipelines
- **No Unnecessary Decoder Weights**: Trimmed for efficient vision-specific applications

## Motivation
The Pixtral-12B Vision Encoder module is designed for researchers and developers who:
- Require high-quality visual feature extraction
- Want to use the vision encoder independently of the full multimodal model
- Seek to implement custom downstream vision tasks
- Desire a lightweight, efficient vision representation module

## Installation
```python
from transformers import AutoModel
import torch

# Load the vision encoder
vision_encoder = AutoModel.from_pretrained("your-repository/pixtral-12b-vision-encoder")
```

## Example Usage
```python
from PIL import Image
import torch

# Load an image
image = Image.open("example_image.jpg")

# Preprocess the image (ensure to use the corresponding processor)
inputs = vision_processor(images=image, return_tensors="pt")

# Extract visual features
with torch.no_grad():
    visual_embeddings = vision_encoder(**inputs).last_hidden_state

# Now you can use visual_embeddings for downstream tasks
```

## Capabilities
- High-quality visual feature extraction
- Support for various image sizes
- Robust representation learning
- Compatible with multiple vision downstream tasks

## Limitations
- Designed specifically for feature extraction
- Performance may vary depending on the specific downstream task
- Requires careful preprocessing and task-specific fine-tuning

## Acknowledgements
Special thanks to the Mistral AI team for developing the original Pixtral-12B multimodal model.

## License
Distributed under the Apache 2.0 License.

## Citation
If you use this vision encoder in your research, please cite the original Mistral AI Pixtral-12B model.