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 1.56 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/059412f834afb0b0c8f885d7f9c0bec0bff97254/texts.json
- Command line
-
hf download hf://dilip025/dummy-model@059412f834afb0b0c8f885d7f9c0bec0bff97254/texts.json
-
curl -L -o texts.json https://huggingface.co/dilip025/dummy-model/resolve/059412f834afb0b0c8f885d7f9c0bec0bff97254/texts.json
1.56 GB
- Xet hash:
- 2039a72e5f371791137fd99b5fea0dcd4e01cdd232515880b386db9ae8eecb40
- Size of remote file:
- 1.56 GB
- SHA256:
- fbce73593423d97b3fdb82537b5a365f8edf9f49e4329b9e6a2f992bb14b61a8
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