Instructions to use codingJacob/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codingJacob/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="codingJacob/dummy-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("codingJacob/dummy-model") model = AutoModelForMaskedLM.from_pretrained("codingJacob/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from codingJacob/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/codingJacob/dummy-model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://codingJacob/dummy-model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/codingJacob/dummy-model/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 2e108fce5c80f9e01ea9d440eae98d7d8cedc9dbc42f7e2bdc4f68f0e603097e
- Size of remote file:
- 443 MB
- SHA256:
- 44c0e284056e40c691db9f54c0502df836365769cb2d64c7d60d0e50a21a9b45
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