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")# pip install -U transformers accelerate # 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_datasetii1.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 1.01 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/cbbf37fdd5e5fbb1403fc6db79429d6304e792c2/tokenized_train_datasetii1.pt
- Command line
-
hf download hf://dilip025/dummy-model@cbbf37fdd5e5fbb1403fc6db79429d6304e792c2/tokenized_train_datasetii1.pt
-
curl -L -o tokenized_train_datasetii1.pt https://huggingface.co/dilip025/dummy-model/resolve/cbbf37fdd5e5fbb1403fc6db79429d6304e792c2/tokenized_train_datasetii1.pt
1.01 GB
- Xet hash:
- 0189f94ae41d9ec1360de97d48c769e56111eae6decef4de788eb082605d75c1
- Size of remote file:
- 1.01 GB
- SHA256:
- 2471605db7390678bb1dae1ca63c3bbdfe98e208c8ad3c331b5813d1f2ae6440
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