Instructions to use kejian/downy-conditional with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kejian/downy-conditional with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kejian/downy-conditional") model = AutoModel.from_pretrained("kejian/downy-conditional", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kejian/downy-conditional: direct link, hf CLI and curl.
- Browser
- Download file 457 MB
-
https://huggingface.co/kejian/downy-conditional/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kejian/downy-conditional/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kejian/downy-conditional/resolve/main/pytorch_model.bin
457 MB
- Xet hash:
- c462e9c4130149c6b6e11410ca3a94ca940328e0317266c4f2f0968260089a11
- Size of remote file:
- 457 MB
- SHA256:
- b98551a859e8b28ae5981d657d6e361ee0e13f58c117ae451e155d7003195319
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.