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 3.28 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/71921c912c083fe7020b435dfcf99d3ba952f7c4/tokenized_train_datasetii.pt
- Command line
-
hf download hf://dilip025/dummy-model@71921c912c083fe7020b435dfcf99d3ba952f7c4/tokenized_train_datasetii.pt
-
curl -L -o tokenized_train_datasetii.pt https://huggingface.co/dilip025/dummy-model/resolve/71921c912c083fe7020b435dfcf99d3ba952f7c4/tokenized_train_datasetii.pt
3.28 GB
- Xet hash:
- 6bc7adffe242d6726a741f5bef8be68d6008050005c18b243553370d4cc6069d
- Size of remote file:
- 3.28 GB
- SHA256:
- 4e4f8792c621f8c4dd4b589473d25b756f8ed1e818ead90453f8a2870374ae42
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