Instructions to use dilip025/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dilip025/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dilip025/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dilip025/dummy-model") model = AutoModelForMaskedLM.from_pretrained("dilip025/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenized_train_datasetii.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 1.01 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/f77a10a7965b855555a70642b46c6d579d4e17e4/tokenized_train_datasetii.pt
- Command line
-
hf download hf://dilip025/dummy-model@f77a10a7965b855555a70642b46c6d579d4e17e4/tokenized_train_datasetii.pt
-
curl -L -o tokenized_train_datasetii.pt https://huggingface.co/dilip025/dummy-model/resolve/f77a10a7965b855555a70642b46c6d579d4e17e4/tokenized_train_datasetii.pt
1.01 GB
- Xet hash:
- ca7f794c18bde311b3e49a35e846fa96e008a2a6eef87423ffcde61f1402579c
- Size of remote file:
- 1.01 GB
- SHA256:
- 1a8da695882a38c3033d67c81ecf10385a37016108e3c5cc973b22ea4fb2ca5f
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