Instructions to use mlx-community/clip-vit-base-patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/clip-vit-base-patch16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir clip-vit-base-patch16 mlx-community/clip-vit-base-patch16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| license: apache-2.0 | |
| library_name: mlx | |
| # mlx-community/clip-vit-base-patch16 | |
| This model was converted to MLX format from [`clip-vit-base-patch16`](https://huggingface.co/openai/clip-vit-base-patch16). | |
| Refer to the [original model card](https://huggingface.co/openai/clip-vit-base-patch16) for more details on the model. | |
| ## Use with mlx-examples | |
| Download the repository 👇 | |
| ``` | |
| pip install huggingface_hub hf_transfer | |
| export HF_HUB_ENABLE_HF_TRANSFER=1 | |
| huggingface-cli download --local-dir <LOCAL FOLDER PATH> mlx-community/clip-vit-base-patch16 | |
| ``` | |
| Install `mlx-examples`. | |
| ```bash | |
| git clone git@github.com:ml-explore/mlx-examples.git | |
| cd clip | |
| pip install -r requirements.txt | |
| ``` | |
| Run the model. | |
| ```python | |
| from PIL import Image | |
| import clip | |
| model, tokenizer, img_processor = clip.load("mlx_model") | |
| inputs = { | |
| "input_ids": tokenizer(["a photo of a cat", "a photo of a dog"]), | |
| "pixel_values": img_processor( | |
| [Image.open("assets/cat.jpeg"), Image.open("assets/dog.jpeg")] | |
| ), | |
| } | |
| output = model(**inputs) | |
| # Get text and image embeddings: | |
| text_embeds = output.text_embeds | |
| image_embeds = output.image_embeds | |
| ``` |