Instructions to use unsloth/ModernBERT-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/ModernBERT-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="unsloth/ModernBERT-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("unsloth/ModernBERT-large") model = AutoModelForMaskedLM.from_pretrained("unsloth/ModernBERT-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from unsloth/ModernBERT-large: direct link, hf CLI and curl.
- Browser
- Download file 1.58 GB
-
https://huggingface.co/unsloth/ModernBERT-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://unsloth/ModernBERT-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/unsloth/ModernBERT-large/resolve/main/pytorch_model.bin
1.58 GB
- Xet hash:
- 486bde6e74a6f6db13c9d4f87252e24f1fa078204dcce417745cb245b98db49b
- Size of remote file:
- 1.58 GB
- SHA256:
- fabb014a0b62d28ec5f2de530d2ade920b9293f170a4ec38acfa1b8096531adc
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