Instructions to use james-burton/kick_32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use james-burton/kick_32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="james-burton/kick_32")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("james-burton/kick_32") model = AutoModelForSequenceClassification.from_pretrained("james-burton/kick_32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from james-burton/kick_32: direct link, hf CLI and curl.
- Browser
- Download file 568 MB
-
https://huggingface.co/james-burton/kick_32/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://james-burton/kick_32@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/james-burton/kick_32/resolve/refs%2Fpr%2F1/pytorch_model.bin
568 MB
- Xet hash:
- 7a61d75f595c3cbdcde3aa5363e8a8e75e6a91112cd6dbe8efb19fd94811c629
- Size of remote file:
- 568 MB
- SHA256:
- 5b48e43e217de8728b016cb5a0f5a225a5f9853e911900f296a83a7df9480b55
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