Instructions to use magicslabnu/Clip_OutEffHop_bert_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use magicslabnu/Clip_OutEffHop_bert_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="magicslabnu/Clip_OutEffHop_bert_base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("magicslabnu/Clip_OutEffHop_bert_base", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("magicslabnu/Clip_OutEffHop_bert_base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from magicslabnu/Clip_OutEffHop_bert_base: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/magicslabnu/Clip_OutEffHop_bert_base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://magicslabnu/Clip_OutEffHop_bert_base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/magicslabnu/Clip_OutEffHop_bert_base/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 0c85afa9e56d9cea371930fe9bfcd41ab592c673553ffa92c31406d649d144dd
- Size of remote file:
- 438 MB
- SHA256:
- f53a1ca477bc2ab951336fa99050d2ecde5ba4027f717128a28faa1e9171f9b6
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