Instructions to use deepset/gelectra-large-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/gelectra-large-generator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="deepset/gelectra-large-generator")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("deepset/gelectra-large-generator") model = AutoModelForMaskedLM.from_pretrained("deepset/gelectra-large-generator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from deepset/gelectra-large-generator: direct link, hf CLI and curl.
- Browser
- Download file 208 MB
-
https://huggingface.co/deepset/gelectra-large-generator/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://deepset/gelectra-large-generator/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/deepset/gelectra-large-generator/resolve/main/pytorch_model.bin
208 MB
- Xet hash:
- a3e617f7dc22247c3a0ed483ec4d8ae022303f4084df640a3f6cf2e5948172a4
- Size of remote file:
- 208 MB
- SHA256:
- e91442289d32da591c2b1299259a24f229f9eadb959e55f07da448aaed34039c
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