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_13000.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_13000.pt
- Command line
-
hf download hf://dilip025/dummy-model@ed10108ac2b9d53434695278f66eaffe49f977af/ir_checkpoints/step_lightning_13000.pt
-
curl -L -o step_lightning_13000.pt https://huggingface.co/dilip025/dummy-model/resolve/ed10108ac2b9d53434695278f66eaffe49f977af/ir_checkpoints/step_lightning_13000.pt
3.39 GB
- Xet hash:
- 62c68c1c8c787497bfe92487da724a21c161b3e94f27323c8d5d2d96779e1ef7
- Size of remote file:
- 3.39 GB
- SHA256:
- 1212c43a618fa21b42ddbb43b7a219bcbd7332541242e0666be077b1fae4bad8
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