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_final_small_39403.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 2.81 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/18a1b958d1b0485d4687f79c7f2d9b9467787158/satori_prajna_checkpoints/step_final_small_39403.pt
- Command line
-
hf download hf://dilip025/dummy-model@18a1b958d1b0485d4687f79c7f2d9b9467787158/satori_prajna_checkpoints/step_final_small_39403.pt
-
curl -L -o step_final_small_39403.pt https://huggingface.co/dilip025/dummy-model/resolve/18a1b958d1b0485d4687f79c7f2d9b9467787158/satori_prajna_checkpoints/step_final_small_39403.pt
2.81 GB
- Xet hash:
- 6dd75e0d7e5090b516c83988ad8a786b9323a7f339feb40479ecfbc47a7bfb25
- Size of remote file:
- 2.81 GB
- SHA256:
- a710dec4d821149e973807a744a420137679dc21fd5e916f742ebbfcd1582b69
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