Instructions to use Vikhrmodels/Borealis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vikhrmodels/Borealis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Vikhrmodels/Borealis", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/Borealis", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Vikhrmodels/Borealis: direct link, hf CLI and curl.
- Browser
- Download file 2.27 GB
-
https://huggingface.co/Vikhrmodels/Borealis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Vikhrmodels/Borealis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Vikhrmodels/Borealis/resolve/main/pytorch_model.bin
2.27 GB
- Xet hash:
- c38eb8433368f52a9e9d71ebb0e6b3eb4f2bb98c3203d8466198321052512799
- Size of remote file:
- 2.27 GB
- SHA256:
- babfecb8b4346c60c9b3fe01e38186bfec189db26626c37d169c008b57fba8ff
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