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 2.07 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/18a1b958d1b0485d4687f79c7f2d9b9467787158/texts.json
- Command line
-
hf download hf://dilip025/dummy-model@18a1b958d1b0485d4687f79c7f2d9b9467787158/texts.json
-
curl -L -o texts.json https://huggingface.co/dilip025/dummy-model/resolve/18a1b958d1b0485d4687f79c7f2d9b9467787158/texts.json
2.07 GB
- Xet hash:
- 21f14fcde0ab067f009acec84727c5e4a31b2e36f91c248a566f0bbe4d7aefbd
- Size of remote file:
- 2.07 GB
- SHA256:
- 3b947311433f0b52e1df4c7834790f15634054d5c2f5d79aa66b5ea7f0507ada
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