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 pytorch_model.bin from roscazo/vih_model: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/roscazo/vih_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://roscazo/vih_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/roscazo/vih_model/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 08639d6946e22ca5369861293f4c4d7a80ce0429210aff1e2380b10dbf53640d
- Size of remote file:
- 499 MB
- SHA256:
- 1bcdd79680dc1a89be44c6c9705466f343b48070df0cd878a968d629736045ae
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