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 satori_tiny_checkpoints/step_final_small_5976.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.99 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/24323c1ac9a2b629789bda8d00b68237de0c3772/satori_tiny_checkpoints/step_final_small_5976.pt
- Command line
-
hf download hf://dilip025/dummy-model@24323c1ac9a2b629789bda8d00b68237de0c3772/satori_tiny_checkpoints/step_final_small_5976.pt
-
curl -L -o step_final_small_5976.pt https://huggingface.co/dilip025/dummy-model/resolve/24323c1ac9a2b629789bda8d00b68237de0c3772/satori_tiny_checkpoints/step_final_small_5976.pt
3.99 GB
- Xet hash:
- 71289bbdcedbcf2962d634fa61bad53aa976d384980e04dce8945228dd3fac61
- Size of remote file:
- 3.99 GB
- SHA256:
- 63917ebf7b532b84a36558d6fcdaba4e8b150ca3c8ff81b55295871436bca0fa
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