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 satori_prajna_checkpoints/step_lightning_13000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 2.81 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/b0cabf8afeef7eb6b72f215e699e99e655515084/satori_prajna_checkpoints/step_lightning_13000.pt
- Command line
-
hf download hf://dilip025/dummy-model@b0cabf8afeef7eb6b72f215e699e99e655515084/satori_prajna_checkpoints/step_lightning_13000.pt
-
curl -L -o step_lightning_13000.pt https://huggingface.co/dilip025/dummy-model/resolve/b0cabf8afeef7eb6b72f215e699e99e655515084/satori_prajna_checkpoints/step_lightning_13000.pt
2.81 GB
- Xet hash:
- 038c585a07988acf78998fbe83a8b20b21d738dc0d5263f7bd3adf3e5f8d5bbd
- Size of remote file:
- 2.81 GB
- SHA256:
- ad8e050db527c7bbce2d16c73c2879e7fda18da2df144e8428a05697d756e678
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