Instructions to use hansgun/model_test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hansgun/model_test2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hansgun/model_test2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hansgun/model_test2") model = AutoModel.from_pretrained("hansgun/model_test2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hansgun/model_test2: direct link, hf CLI and curl.
- Browser
- Download file 369 MB
-
https://huggingface.co/hansgun/model_test2/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://hansgun/model_test2@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hansgun/model_test2/resolve/refs%2Fpr%2F1/pytorch_model.bin
369 MB
- Xet hash:
- 1b728f83bba429d860d9e21599fed8f9aadb7a593419f0fbde0df9e03c32c8e5
- Size of remote file:
- 369 MB
- SHA256:
- e4a05b82956c9074cb38bfdbf89d9854d361a48bbcda1087147e2886fa562bc2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.