Instructions to use roscazo/vih_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roscazo/vih_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="roscazo/vih_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("roscazo/vih_test") model = AutoModelForSequenceClassification.from_pretrained("roscazo/vih_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from roscazo/vih_test: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/roscazo/vih_test/resolve/main/training_args.bin
- Command line
-
hf download hf://roscazo/vih_test/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/roscazo/vih_test/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- bc113b8aa9c6df733bed8c4a4439087b9e1b8a7103fd1c3e833a15b51c45e8fb
- Size of remote file:
- 4.86 kB
- SHA256:
- d9b9dae9900de90fbfee805bbab71d6dec610e46fb7e422fa973c38ada392e5e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.