Instructions to use gszabo/large2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gszabo/large2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gszabo/large2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gszabo/large2") model = AutoModelForMaskedLM.from_pretrained("gszabo/large2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gszabo/large2: direct link, hf CLI and curl.
- Browser
- Download file 1.59 GB
-
https://huggingface.co/gszabo/large2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gszabo/large2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gszabo/large2/resolve/main/pytorch_model.bin
1.59 GB
- Xet hash:
- 5546ed384bb48541ee3065988aaa1dfd2142b1e2b3738a09bb66bd09ad761c94
- Size of remote file:
- 1.59 GB
- SHA256:
- f93dc9a3e79c4d6ab9ce92c358fc2a35fe6fc8cb01128d8114c1624d2e1c959e
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