Instructions to use yongsun-yoon/koenbert-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yongsun-yoon/koenbert-dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="yongsun-yoon/koenbert-dev")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("yongsun-yoon/koenbert-dev") model = AutoModel.from_pretrained("yongsun-yoon/koenbert-dev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from yongsun-yoon/koenbert-dev: direct link, hf CLI and curl.
- Browser
- Download file 530 MB
-
https://huggingface.co/yongsun-yoon/koenbert-dev/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://yongsun-yoon/koenbert-dev/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/yongsun-yoon/koenbert-dev/resolve/main/pytorch_model.bin
530 MB
- Xet hash:
- 17a4e89fb1bdd569bd82abcd5557b6ec72d0b8ff7a0bef4c0fb4cf678d9a5000
- Size of remote file:
- 530 MB
- SHA256:
- 36bcf90cb0b608ec53817947f63b1a14d23dc4dff909bd21bc5303653f66d9bb
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