Instructions to use jmilic/adapter_bottleneck_final-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jmilic/adapter_bottleneck_final-2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jmilic/adapter_bottleneck_final-2") model = AutoModel.from_pretrained("jmilic/adapter_bottleneck_final-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jmilic/adapter_bottleneck_final-2: direct link, hf CLI and curl.
- Browser
- Download file 505 MB
-
https://huggingface.co/jmilic/adapter_bottleneck_final-2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jmilic/adapter_bottleneck_final-2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jmilic/adapter_bottleneck_final-2/resolve/main/pytorch_model.bin
505 MB
- Xet hash:
- f454b480dd537ca7cc3e2e2143958f8c349f8b6b13ee20ce0a1fd6d674967ed8
- Size of remote file:
- 505 MB
- SHA256:
- d47c43dc99d073d130e519538e7b3f573c5eda462398d479737d5c16b28dbb30
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.