Instructions to use google/electra-small-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/electra-small-generator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google/electra-small-generator")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google/electra-small-generator") model = AutoModelForMaskedLM.from_pretrained("google/electra-small-generator", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/electra-small-generator: direct link, hf CLI and curl.
- Browser
- Download file 54.2 MB
-
https://huggingface.co/google/electra-small-generator/resolve/refs%2Fpr%2F2/flax_model.msgpack
- Command line
-
hf download hf://google/electra-small-generator@refs/pr/2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/electra-small-generator/resolve/refs%2Fpr%2F2/flax_model.msgpack
54.2 MB
- Xet hash:
- 70c44f6ee7ba59ebc769d4b1972c55f3f81a78bde219299874dc8c684463886c
- Size of remote file:
- 54.2 MB
- SHA256:
- bf4a49d8541561372550fc94db6847b52447fc339dc1b95717809024fae36ad1
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