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 texts.json from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 1.55 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/bbf8ed253f162fd7a66199b38894d5433daeb079/texts.json
- Command line
-
hf download hf://dilip025/dummy-model@bbf8ed253f162fd7a66199b38894d5433daeb079/texts.json
-
curl -L -o texts.json https://huggingface.co/dilip025/dummy-model/resolve/bbf8ed253f162fd7a66199b38894d5433daeb079/texts.json
1.55 GB
- Xet hash:
- 0151336e8ea5f13442378f6a14d9b256a79cbb8c0cba240a14d4eca3ce4e5f1e
- Size of remote file:
- 1.55 GB
- SHA256:
- b17eb6301402c0368b88cedaa5158dd08764213123f1674d086eed4afe86c55a
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