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_small_checkpoints/step_final_small_2802.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/main/satori_small_checkpoints/step_final_small_2802.pt
- Command line
-
hf download hf://dilip025/dummy-model/satori_small_checkpoints/step_final_small_2802.pt
-
curl -L -o step_final_small_2802.pt https://huggingface.co/dilip025/dummy-model/resolve/main/satori_small_checkpoints/step_final_small_2802.pt
6.29 GB
- Xet hash:
- 74ecf0d5536400570a43c4fd2967767a96e70ef16d6affd4f9c22f6f7ca04289
- Size of remote file:
- 6.29 GB
- SHA256:
- f201740b23397d7e036bf4f4249e288842b6a4167d90b9bce801e1f35617c7f5
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