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_mid_checkpoints/step_lightning_2200.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.49 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/bd96ce10f26773e3c581ae2e17091cbb72b617bd/satori_mid_checkpoints/step_lightning_2200.pt
- Command line
-
hf download hf://dilip025/dummy-model@bd96ce10f26773e3c581ae2e17091cbb72b617bd/satori_mid_checkpoints/step_lightning_2200.pt
-
curl -L -o step_lightning_2200.pt https://huggingface.co/dilip025/dummy-model/resolve/bd96ce10f26773e3c581ae2e17091cbb72b617bd/satori_mid_checkpoints/step_lightning_2200.pt
4.49 GB
- Xet hash:
- 9f6eb40cbd9a40fc0863f4734d675cf08b291fb70e99b9a166c42dd71f766576
- Size of remote file:
- 4.49 GB
- SHA256:
- 540769de956689b48c493ddb1b1335665a56c768837957062e32835ca3f4794d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.