Instructions to use roscazo/vih_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roscazo/vih_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="roscazo/vih_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("roscazo/vih_model") model = AutoModelForSequenceClassification.from_pretrained("roscazo/vih_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from roscazo/vih_model: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/roscazo/vih_model/resolve/main/training_args.bin
- Command line
-
hf download hf://roscazo/vih_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/roscazo/vih_model/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 9cc1e5d5ac227d4791e9e6852790a56908aaa99845c8c31a664c0e75634cc985
- Size of remote file:
- 4.92 kB
- SHA256:
- f56a2c054c9c4c311501ab2dc2dd643e29c71c596b357ba014260b92ac7d4d01
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.