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")# 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 ir_checkpoints/step_lightning_12000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.39 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/753e038cc4fa96e26366e932b22f8c524c1ed8a6/ir_checkpoints/step_lightning_12000.pt
- Command line
-
hf download hf://dilip025/dummy-model@753e038cc4fa96e26366e932b22f8c524c1ed8a6/ir_checkpoints/step_lightning_12000.pt
-
curl -L -o step_lightning_12000.pt https://huggingface.co/dilip025/dummy-model/resolve/753e038cc4fa96e26366e932b22f8c524c1ed8a6/ir_checkpoints/step_lightning_12000.pt
3.39 GB
- Xet hash:
- 9ab0dd4748e6150b7de0a25627b9036776173b400bb5359e0aacdba6e6c93bd6
- Size of remote file:
- 3.39 GB
- SHA256:
- 557c2f0aad25a35e3c8a6142d6c480f9d7303cf459f917182eb44f2ae2b8a979
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