Instructions to use witiko/mathberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use witiko/mathberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="witiko/mathberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("witiko/mathberta") model = AutoModelForMaskedLM.from_pretrained("witiko/mathberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from witiko/mathberta: direct link, hf CLI and curl.
- Browser
- Download file 586 MB
-
https://huggingface.co/witiko/mathberta/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://witiko/mathberta@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/witiko/mathberta/resolve/refs%2Fpr%2F1/pytorch_model.bin
586 MB
- Xet hash:
- 6a3dfbd7fbf0572f200547f038780b982ff97109b3b43dad79f8a4994a2eaf70
- Size of remote file:
- 586 MB
- SHA256:
- 8d80d16ee130a9f3156cc13609208c69b30de3353622b5cd078885499c5ded88
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