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 516 MB
-
https://huggingface.co/dilip025/dummy-model/resolve/cbbf37fdd5e5fbb1403fc6db79429d6304e792c2/texts.json
- Command line
-
hf download hf://dilip025/dummy-model@cbbf37fdd5e5fbb1403fc6db79429d6304e792c2/texts.json
-
curl -L -o texts.json https://huggingface.co/dilip025/dummy-model/resolve/cbbf37fdd5e5fbb1403fc6db79429d6304e792c2/texts.json
516 MB
- Xet hash:
- aa8ce297c377c4b077a94d6b31c61567fb77e830ec91e9440f8176e434a5e4cc
- Size of remote file:
- 516 MB
- SHA256:
- cbf2e2828458146e943ce2063283d1d2739b3f580b82e40f15bca5d3507d7515
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