Instructions to use grantpitt/autotagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grantpitt/autotagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="grantpitt/autotagger")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("grantpitt/autotagger") model = AutoModel.from_pretrained("grantpitt/autotagger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from grantpitt/autotagger: direct link, hf CLI and curl.
- Browser
- Download file 852 MB
-
https://huggingface.co/grantpitt/autotagger/resolve/8e8575118be6317d9fc605d2075b9d442821bdfa/pytorch_model.bin
- Command line
-
hf download hf://grantpitt/autotagger@8e8575118be6317d9fc605d2075b9d442821bdfa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/grantpitt/autotagger/resolve/8e8575118be6317d9fc605d2075b9d442821bdfa/pytorch_model.bin
852 MB
- Xet hash:
- 1184d3e29edb5cccb2dd2aba4ca3a00c721471b51c142b8500382beb3a8baaa8
- Size of remote file:
- 852 MB
- SHA256:
- 3a70809863e36b809d7873e13cced36e735ec485d90b2e989d73bd47fb2e93e7
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