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_final_small_30000.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_final_small_30000.pt
- Command line
-
hf download hf://dilip025/dummy-model@ed10108ac2b9d53434695278f66eaffe49f977af/satori_prajna_checkpoints/step_final_small_30000.pt
-
curl -L -o step_final_small_30000.pt https://huggingface.co/dilip025/dummy-model/resolve/ed10108ac2b9d53434695278f66eaffe49f977af/satori_prajna_checkpoints/step_final_small_30000.pt
2.81 GB
- Xet hash:
- 16ce3f68da757ac8042a3b5e0ec3e0dbbeb8c4e0187e9fd8daec7d9dea800037
- Size of remote file:
- 2.81 GB
- SHA256:
- 05cbbed0f349833c8eb35c5e7169d50cc6bddfe6ffb1c4e392817c954afb5eb5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.