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/21f65e3a9e7502ebaa2952b5dc0d045550cef245/texts.json
- Command line
-
hf download hf://dilip025/dummy-model@21f65e3a9e7502ebaa2952b5dc0d045550cef245/texts.json
-
curl -L -o texts.json https://huggingface.co/dilip025/dummy-model/resolve/21f65e3a9e7502ebaa2952b5dc0d045550cef245/texts.json
1.56 GB
- Xet hash:
- 32523b8449afcb4da785af79b723fd729af07bae3106d3c5e3385a24091eed64
- Size of remote file:
- 1.56 GB
- SHA256:
- d2e542d896d45ede8c4e2b9fe86c03aa5742b25d04a563a2a98ace0c404155a1
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