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")# pip install -U transformers accelerate # 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 satori_prajna_checkpoints/step_lightning_5000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 2.81 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/ed10108ac2b9d53434695278f66eaffe49f977af/satori_prajna_checkpoints/step_lightning_5000.pt
- Command line
-
hf download hf://dilip025/dummy-model@ed10108ac2b9d53434695278f66eaffe49f977af/satori_prajna_checkpoints/step_lightning_5000.pt
-
curl -L -o step_lightning_5000.pt https://huggingface.co/dilip025/dummy-model/resolve/ed10108ac2b9d53434695278f66eaffe49f977af/satori_prajna_checkpoints/step_lightning_5000.pt
2.81 GB
- Xet hash:
- 8114c97169c708f4f1f6f77dd88bc3437f7e196c433cbfe7230cff711599e836
- Size of remote file:
- 2.81 GB
- SHA256:
- 77ca78a2187e09bdc6c0c31d216356d9890916f48d628a96fa9fde590eb0a2d7
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