Instructions to use rjac-ml/antler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rjac-ml/antler with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="rjac-ml/antler")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("rjac-ml/antler") model = AutoModel.from_pretrained("rjac-ml/antler", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rjac-ml/antler: direct link, hf CLI and curl.
- Browser
- Download file 166 MB
-
https://huggingface.co/rjac-ml/antler/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rjac-ml/antler/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rjac-ml/antler/resolve/main/pytorch_model.bin
166 MB
- Xet hash:
- 87380e74cc7c0074b8027de055ceba60193b92c0c2768db22e6512c1cb003474
- Size of remote file:
- 166 MB
- SHA256:
- 1b49df037dcd4f56b527c06e3c29e439b786f6eb841657ec1c34d03455cdd920
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.