Instructions to use harinib/model_los with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harinib/model_los with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="harinib/model_los")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("harinib/model_los") model = AutoModelForSequenceClassification.from_pretrained("harinib/model_los", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from harinib/model_los: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/harinib/model_los/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://harinib/model_los/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/harinib/model_los/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- f5c803394ed22f0d9e75ce65b7b1e1b1ff118a1aaf6856350bbcc76c117d16da
- Size of remote file:
- 268 MB
- SHA256:
- cf7a566da1c67f27f37440a5c4dc70841cbc387da09668eedbf656e479ee3c2b
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