Instructions to use krevas/finance-electra-small-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krevas/finance-electra-small-generator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="krevas/finance-electra-small-generator")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("krevas/finance-electra-small-generator") model = AutoModelForMaskedLM.from_pretrained("krevas/finance-electra-small-generator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from krevas/finance-electra-small-generator: direct link, hf CLI and curl.
- Browser
- Download file 64.1 MB
-
https://huggingface.co/krevas/finance-electra-small-generator/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://krevas/finance-electra-small-generator/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/krevas/finance-electra-small-generator/resolve/main/pytorch_model.bin
64.1 MB
- Xet hash:
- 11fb09bfc44df65252f77b417c26bf5d06b808a62da1a15aace7c461b8dad26e
- Size of remote file:
- 64.1 MB
- SHA256:
- c7edac24b03ee24a06aa5204233ed9483cc7d4d8a84d713f5e3acb60f3e3b7a9
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