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 tokenizer.json from Vikhrmodels/Borealis: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Vikhrmodels/Borealis/resolve/refs%2Fpr%2F4/tokenizer.json
- Command line
-
hf download hf://Vikhrmodels/Borealis@refs/pr/4/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Vikhrmodels/Borealis/resolve/refs%2Fpr%2F4/tokenizer.json
11.4 MB
- Xet hash:
- 43281486ef4fcad4de7c826a16a5aeffba5494ff9c2fc176cdd21e7ba3a1e3a0
- Size of remote file:
- 11.4 MB
- SHA256:
- 55c7fad3b807310f01cead0edd8fa225070d199053eb0649e31f58a1caf09aa2
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