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 3.25 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/ac08983d02011e3844fb2174d913b39a054de6d3/tokenized_train_datasetii.pt
- Command line
-
hf download hf://dilip025/dummy-model@ac08983d02011e3844fb2174d913b39a054de6d3/tokenized_train_datasetii.pt
-
curl -L -o tokenized_train_datasetii.pt https://huggingface.co/dilip025/dummy-model/resolve/ac08983d02011e3844fb2174d913b39a054de6d3/tokenized_train_datasetii.pt
3.25 GB
- Xet hash:
- beb75d780dfa3f62cbf55fa1b96ba887d10210da9ed5b29879bbaa892ab5b96d
- Size of remote file:
- 3.25 GB
- SHA256:
- f09f9f903e483fa388c8fe0e6e53d899ba5e5f1ff780b49761994689884bed4c
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