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 1.56 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/266d5883061eb4dbca9768e42a68ee0dbbe3af1f/texts.json
- Command line
-
hf download hf://dilip025/dummy-model@266d5883061eb4dbca9768e42a68ee0dbbe3af1f/texts.json
-
curl -L -o texts.json https://huggingface.co/dilip025/dummy-model/resolve/266d5883061eb4dbca9768e42a68ee0dbbe3af1f/texts.json
1.56 GB
- Xet hash:
- 1d05b23e6e0cb345982d7dd6c2ab4f7541ff110fe26b2416d9dd5bb891d4ebe1
- Size of remote file:
- 1.56 GB
- SHA256:
- 9fadd24f686ef18b7ce29487f014496ba203a85c56807c08cac994857093e2c4
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