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library_name: aneforge
base_model: openai/clip-vit-base-patch32
pipeline_tag: zero-shot-image-classification
tags:
- aneforge
- apple-neural-engine
- clip
---
# CLIP ViT-B/32 (ANEForge)
An unmodified duplicate of [`openai/clip-vit-base-patch32`](https://huggingface.co/openai/clip-vit-base-patch32), tagged for use with
[**ANEForge**](https://github.com/sbryngelson/ANEForge) so the weights load and run directly on the
Apple Neural Engine (no CoreML). Weights are byte-identical to the source; see the original repo for
the model details. See the original repo for license terms.
## Use with ANEForge
```python
# pip install "aneforge[models]"
import aneforge as af
clip = af.load_clip("aneforge/clip-vit-base-patch32")
print(clip.classify(image, ["a photo of a cat", "a photo of a dog"])) # image: a PIL.Image; zero-shot (label, prob)
```
ANEForge compiles the model's graph into a single ANE program and streams the weights from this repo
via `huggingface_hub`. See the [docs](https://aneforge.readthedocs.io) and the [paper](https://arxiv.org/abs/2606.17090).
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