Instructions to use PS4Research/marimo-workshop-clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PS4Research/marimo-workshop-clip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="PS4Research/marimo-workshop-clip") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("PS4Research/marimo-workshop-clip") model = AutoModelForZeroShotImageClassification.from_pretrained("PS4Research/marimo-workshop-clip", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 946 Bytes
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license: mit
library_name: transformers
pipeline_tag: zero-shot-image-classification
base_model: openai/clip-vit-base-patch32
tags:
- clip
- marimo
- workshop
---
# CLIP ViT-B/32 for the marimo workshop
An unmodified copy of [`openai/clip-vit-base-patch32`](https://huggingface.co/openai/clip-vit-base-patch32) (revision `3d74acf9a28c67741b2f4f2ea7635f0aaf6f0268`),
saved as safetensors with its pre-processor. It is pinned here so every workshop notebook loads exactly the same
weights that produced the vectors in
[`PS4Research/marimo-workshop-catalogue`](https://huggingface.co/datasets/PS4Research/marimo-workshop-catalogue).
```python
from transformers import CLIPModel, CLIPProcessor
model = CLIPModel.from_pretrained("PS4Research/marimo-workshop-clip")
processor = CLIPProcessor.from_pretrained("PS4Research/marimo-workshop-clip")
```
No fine-tuning was done. See the original model card for intended uses, limitations and biases.
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