Instructions to use ivensamdh/dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ivensamdh/dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ivensamdh/dev") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ivensamdh/dev") model = AutoModelForImageClassification.from_pretrained("ivensamdh/dev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from ivensamdh/dev: direct link, hf CLI and curl.
- Browser
- Download file 172 MB
-
https://huggingface.co/ivensamdh/dev/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://ivensamdh/dev/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/ivensamdh/dev/resolve/main/flax_model.msgpack
172 MB
- Xet hash:
- 17640be997249ef6c601b63dac4591e75ac55083d43bb6f44a45d1b64d5d6fb0
- Size of remote file:
- 172 MB
- SHA256:
- 7f50c7059b1fddf66ffdc1e1a96c2a4892740dce6481c082905456bff99ba5b2
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