Instructions to use harinib/model_los_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harinib/model_los_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="harinib/model_los_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("harinib/model_los_2") model = AutoModelForSequenceClassification.from_pretrained("harinib/model_los_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from harinib/model_los_2: direct link, hf CLI and curl.
- Browser
- Download file 126 MB
-
https://huggingface.co/harinib/model_los_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://harinib/model_los_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/harinib/model_los_2/resolve/main/pytorch_model.bin
126 MB
- Xet hash:
- db2cf398001011940ce581d0946c3d9cae4cee0fa712c156b8a0b4ba6988997b
- Size of remote file:
- 126 MB
- SHA256:
- 057c138442a47f7285dc93cb9b709d140a6cb61cc4cafcb24de63d67424bbc16
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