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_18000.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_18000.pt
- Command line
-
hf download hf://dilip025/dummy-model@ed10108ac2b9d53434695278f66eaffe49f977af/ir_checkpoints/step_lightning_18000.pt
-
curl -L -o step_lightning_18000.pt https://huggingface.co/dilip025/dummy-model/resolve/ed10108ac2b9d53434695278f66eaffe49f977af/ir_checkpoints/step_lightning_18000.pt
3.39 GB
- Xet hash:
- 413515bc68cc628e4dd3b9b31675ebe8bfb9545857b512f529514a6373389ff4
- Size of remote file:
- 3.39 GB
- SHA256:
- 7ea256e42e361d2108fd0641c38a346fe183cc24d9f31636caf0c25d7970ff4e
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