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_long.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 1.73 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/a4a814cce3854f06e00e7791950c2dced8c8f3f4/tokenized_train_datasetii_long.pt
- Command line
-
hf download hf://dilip025/dummy-model@a4a814cce3854f06e00e7791950c2dced8c8f3f4/tokenized_train_datasetii_long.pt
-
curl -L -o tokenized_train_datasetii_long.pt https://huggingface.co/dilip025/dummy-model/resolve/a4a814cce3854f06e00e7791950c2dced8c8f3f4/tokenized_train_datasetii_long.pt
1.73 GB
- Xet hash:
- cbf2a6c25e5747f088d9e3fecf48da31c6420a275b6d2cbf76996619b2d761dd
- Size of remote file:
- 1.73 GB
- SHA256:
- f949789d16afd1ca6e808bb80911795ac15815a3970d8f8cdefc3ab75406bac5
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