MLX_CLIP 📚🤖

Welcome to the MLX_CLIP repository! 🎉 This repository contains an implementation of the CLIP (Contrastive Language-Image Pre-training) model using the MLX library. CLIP is a powerful model that learns to associate images with their corresponding textual descriptions, enabling various downstream tasks such as image retrieval and zero-shot classification. 🖼️📝

Features ✨

• Easy-to-use MLX_CLIP model for generating image and text embeddings
• Support for loading pre-trained CLIP weights from Hugging Face
• Efficient conversion of weights to MLX format for optimal performance
• Seamless integration with the MLX library for accelerated inference on Apple Silicon devices

Getting Started 🚀

To get started with MLX_CLIP, follow these steps:

1. Clone the repository:

   git clone https://github.com/harperreed/mlx_clip.git
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Load the pre-trained CLIP model:

   from mlx_clip import mlx_clip
   
   model_dir = "path/to/pretrained/model"
   clip = mlx_clip(model_dir)

4. Use the CLIP model for generating image and text embeddings:

   image_path = "path/to/image.jpg"
   image_embedding = clip.image_encoder(image_path)
   
   text = "A description of the image"
   text_embedding = clip.text_encoder(text)

Examples 💡

Check out the example.py file for a simple example of how to use MLX_CLIP to generate image and text embeddings.

Model Conversion 🔄

MLX_CLIP provides a convenient utility to convert pre-trained CLIP weights from the Hugging Face repository to the MLX format. To convert weights, use the convert_weights function from mlx_clip.convert:

from mlx_clip.convert import convert_weights

hf_repo = "openai/clip-vit-base-patch32"
mlx_path = "path/to/save/converted/model"
convert_weights(hf_repo, mlx_path)

Contributing 🤝

Contributions to MLX_CLIP are welcome! If you encounter any issues, have suggestions for improvements, or want to add new features, please open an issue or submit a pull request. Make sure to follow the existing code style and provide appropriate documentation for your changes.

License 📜

MLX_CLIP is licensed under the MIT License.

Acknowledgments 🙏

MLX_CLIP is heavily based on the mlx-experiments clip implementation. Special thanks to the MLX team for their incredible work!

Contact 📞

For any questions or inquiries, feel free to reach out to the project maintainer:

Harper Reed

• Email: harper@modest.com
• GitHub: harperreed

Happy coding with MLX_CLIP! 😄💻🚀