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_5500.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.39 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/d8269403a2597ca8b68cc65bafd2aabc3cce4fb8/ir_checkpoints/step_lightning_5500.pt
- Command line
-
hf download hf://dilip025/dummy-model@d8269403a2597ca8b68cc65bafd2aabc3cce4fb8/ir_checkpoints/step_lightning_5500.pt
-
curl -L -o step_lightning_5500.pt https://huggingface.co/dilip025/dummy-model/resolve/d8269403a2597ca8b68cc65bafd2aabc3cce4fb8/ir_checkpoints/step_lightning_5500.pt
3.39 GB
- Xet hash:
- 110bb98ca0cce37897e3e11bcd6197fcc9d371abd7459aa94ace7b013175e68f
- Size of remote file:
- 3.39 GB
- SHA256:
- 9d5f35d286c21875b35b64d3f5a1b5791970a8589c5cae568245a134df4f5eab
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