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")# pip install -U transformers accelerate # 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 1.57 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/eef1ccf8c6d6363fa3f51d9cdaabc88d373b27ec/texts.json
- Command line
-
hf download hf://dilip025/dummy-model@eef1ccf8c6d6363fa3f51d9cdaabc88d373b27ec/texts.json
-
curl -L -o texts.json https://huggingface.co/dilip025/dummy-model/resolve/eef1ccf8c6d6363fa3f51d9cdaabc88d373b27ec/texts.json
1.57 GB
- Xet hash:
- 847b91066cea98e6d7d5a9de958c7c7dcda9e76b845a25e24368ee089426acb3
- Size of remote file:
- 1.57 GB
- SHA256:
- 30319caeb293c3669c6f6fcc716ae956d91b6120d2912a1cc728c64dbdad7170
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