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 1.43 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/71921c912c083fe7020b435dfcf99d3ba952f7c4/tokenized_train_datasetii_long.pt
- Command line
-
hf download hf://dilip025/dummy-model@71921c912c083fe7020b435dfcf99d3ba952f7c4/tokenized_train_datasetii_long.pt
-
curl -L -o tokenized_train_datasetii_long.pt https://huggingface.co/dilip025/dummy-model/resolve/71921c912c083fe7020b435dfcf99d3ba952f7c4/tokenized_train_datasetii_long.pt
1.43 GB
- Xet hash:
- f0857917deb693c53359471e72bf681399da33dc894b22d4cc2da86f8cd23cda
- Size of remote file:
- 1.43 GB
- SHA256:
- 71ab9d0aea72393b9155426b2ebc7e840e3b6c105ec951e7701fe7789c27710b
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