Instructions to use tiya1012/bullyxlm2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/bullyxlm2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/bullyxlm2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/bullyxlm2") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/bullyxlm2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/bullyxlm2: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/tiya1012/bullyxlm2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/bullyxlm2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/bullyxlm2/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 91b2340a43b86ed4328ca74d410e7302006caa3fedd25b96e9c91d6f81d24e6a
- Size of remote file:
- 1.11 GB
- SHA256:
- 667a6e97cdf953ea1031b84df038a582923d064a62778e7ce1c37a6af7d9a9aa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.