Instructions to use JunHwi/kmhas_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunHwi/kmhas_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JunHwi/kmhas_binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JunHwi/kmhas_binary") model = AutoModelForSequenceClassification.from_pretrained("JunHwi/kmhas_binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from JunHwi/kmhas_binary: direct link, hf CLI and curl.
- Browser
- Download file 452 MB
-
https://huggingface.co/JunHwi/kmhas_binary/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://JunHwi/kmhas_binary/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/JunHwi/kmhas_binary/resolve/main/pytorch_model.bin
452 MB
- Xet hash:
- 97b53c66f6c835a1c823fc9880db0d646439aaa8b3ae3168b406fa718ab56fa4
- Size of remote file:
- 452 MB
- SHA256:
- e1630b66f7d95cc89d5e4683de30b3bc9f7edc2f3379403a032086f886c514b6
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