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_4500.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.39 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/ed10108ac2b9d53434695278f66eaffe49f977af/ir_checkpoints/step_lightning_4500.pt
- Command line
-
hf download hf://dilip025/dummy-model@ed10108ac2b9d53434695278f66eaffe49f977af/ir_checkpoints/step_lightning_4500.pt
-
curl -L -o step_lightning_4500.pt https://huggingface.co/dilip025/dummy-model/resolve/ed10108ac2b9d53434695278f66eaffe49f977af/ir_checkpoints/step_lightning_4500.pt
3.39 GB
- Xet hash:
- 2477ec5eb57025bc5d692c3feebeabc8ae9a69a36640df4440b9d7fcfb95e2f6
- Size of remote file:
- 3.39 GB
- SHA256:
- 2ee1e60cae560c6925be36eb8a3fc131c59b03de8fbee754ad6c3f3a75eba9f6
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