NbAiLab/borealis2-31b-preview
NbAiLab/borealis2-31b-preview is a 31-billion parameter instruction-tuned preview model developed by NbAiLab. It is based on unsloth/gemma-4-31B-it, continuously pre-trained on a 47-billion word dataset with a focus on Nordic languages, and fine-tuned on textual instructions. This model is designed for Norwegian-centric assistant tasks like drafting, summarization, Q&A, and light reasoning, and is intended for early evaluation of Norwegian language coverage and writing style assessment.
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Borealis 2 31B (Preview) Overview
NbAiLab/borealis2-31b-preview is a 31-billion parameter instruction-tuned model from NbAiLab, released as a preview for early testing and feedback. It is built upon unsloth/gemma-4-31B-it, further pre-trained on the extensive Aurora dataset, and then supervised fine-tuned using NbAiLab/aurora-sft-2606.
Key Characteristics
- Architecture: Based on Gemma 4, with a 31B parameter count.
- Training Data: Continuous pre-training utilized the
Auroradataset, comprising 47 billion whitespace-separated words with a knowledge cutoff of January 1st, 2025. This dataset includes a significant proportion of Norwegian (Bokmål and Nynorsk), Danish, Swedish, Icelandic, Faroese, Finnish, and various Sami languages, alongside English. - Fine-tuning: Supervised fine-tuning was performed for three epochs on textual instructions, with loss calculated only on assistant responses.
- Language Focus: Primarily designed for Norwegian-centric tasks, with coverage for Norwegian (Bokmål, Nynorsk) and English.
Intended Use Cases
- Norwegian Assistant Tasks: Suitable for drafting, summarization, question-answering, and light reasoning in Norwegian.
- Writing Style Assessment: Can be used to evaluate Norwegian writing style and quality.
- Early Evaluation: Ideal for assessing model behavior, language coverage, and overall quality in a preview context.
Important Considerations
As a preview model, Borealis 2 31B is not fully safety-aligned and may produce harmful or biased content. It is not recommended for safety-critical applications and requires additional safety mitigations if deployed. Outputs may be unstable and prone to hallucinations.