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 2.08 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/48eda8ed471559e0d4b0f6da4c7ecdc11dbdc80a/texts.json
- Command line
-
hf download hf://dilip025/dummy-model@48eda8ed471559e0d4b0f6da4c7ecdc11dbdc80a/texts.json
-
curl -L -o texts.json https://huggingface.co/dilip025/dummy-model/resolve/48eda8ed471559e0d4b0f6da4c7ecdc11dbdc80a/texts.json
2.08 GB
- Xet hash:
- e8235eb08c4fdf21eb771122283de191c70de0fe2252c5c9cd0f43ffaab11497
- Size of remote file:
- 2.08 GB
- SHA256:
- b3f2cb9e2aeccd21f8d7e7b50a9941dd5d476107c7f2376c62a4871ab3b4eea0
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