Instructions to use gszabo/base_subtoken1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gszabo/base_subtoken1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gszabo/base_subtoken1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gszabo/base_subtoken1") model = AutoModelForMaskedLM.from_pretrained("gszabo/base_subtoken1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gszabo/base_subtoken1: direct link, hf CLI and curl.
- Browser
- Download file 676 MB
-
https://huggingface.co/gszabo/base_subtoken1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gszabo/base_subtoken1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gszabo/base_subtoken1/resolve/main/pytorch_model.bin
676 MB
- Xet hash:
- 038cecc1bc06165fa581a0f54880e27d57548d04a5b9227c901b94846eaaa6cb
- Size of remote file:
- 676 MB
- SHA256:
- ea5a547b4a17d70b00ca07a7d594065c90a3170192809eabeb53fe5e9caf956e
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