Instructions to use mrfriedpotato/perform_t with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrfriedpotato/perform_t with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mrfriedpotato/perform_t")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mrfriedpotato/perform_t") model = AutoModelForSequenceClassification.from_pretrained("mrfriedpotato/perform_t", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mrfriedpotato/perform_t: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/mrfriedpotato/perform_t/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mrfriedpotato/perform_t/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mrfriedpotato/perform_t/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- f25f7ffcebfc3a2097f6b5b1caf06f00a93453a2ca1f7e29e8147f1a944f24de
- Size of remote file:
- 438 MB
- SHA256:
- 7e19e2a936ed681d926d1a811a6310651d13277153ad7132caed46a14ab2071c
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