Instructions to use PoetschLab/GROVER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PoetschLab/GROVER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="PoetschLab/GROVER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("PoetschLab/GROVER") model = AutoModelForMaskedLM.from_pretrained("PoetschLab/GROVER", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from PoetschLab/GROVER: direct link, hf CLI and curl.
- Browser
- Download file 348 MB
-
https://huggingface.co/PoetschLab/GROVER/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://PoetschLab/GROVER/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/PoetschLab/GROVER/resolve/main/pytorch_model.bin
348 MB
- Xet hash:
- cce2e7df10dde328406a49ad322318d131ea5be0d8f4522b7772ec6ae2356352
- Size of remote file:
- 348 MB
- SHA256:
- 81fa4ee056e65ff1c06c32512589f3ba3bcb9d054960f35f634b9cf6da7aadda
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