Instructions to use NbAiLab/borealis-12b-instruct-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/borealis-12b-instruct-preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="NbAiLab/borealis-12b-instruct-preview") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("NbAiLab/borealis-12b-instruct-preview") model = AutoModelForImageTextToText.from_pretrained("NbAiLab/borealis-12b-instruct-preview") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use NbAiLab/borealis-12b-instruct-preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NbAiLab/borealis-12b-instruct-preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NbAiLab/borealis-12b-instruct-preview", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/NbAiLab/borealis-12b-instruct-preview
- SGLang
How to use NbAiLab/borealis-12b-instruct-preview with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "NbAiLab/borealis-12b-instruct-preview" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NbAiLab/borealis-12b-instruct-preview", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "NbAiLab/borealis-12b-instruct-preview" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NbAiLab/borealis-12b-instruct-preview", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use NbAiLab/borealis-12b-instruct-preview with Docker Model Runner:
docker model run hf.co/NbAiLab/borealis-12b-instruct-preview
Borealis 12B Instruct (Preview)
Release: Jan 31st, 2026.
Model summary
NbAiLab/borealis-12b-instruct-preview is a 12B-parameter instruction-tuned preview model intended for early testing and feedback. It is an experiment and should be treated as pre-release quality.
This model is based on google/gemma-3-12b-it, and fine-tuned on textual instructions only.
Training data
Supervised fine-tuning (SFT) uses NbAiLab/aurora-sft-2512 (not released yet).
⚠️ Safety / alignment disclaimer (important)
This is a preview experiment and has not been safety-aligned yet. The model may produce harmful, biased, or insensitive outputs (including content that is offensive, unsafe, or inappropriate). Do not use it for safety-critical or high-stakes applications, and add your own safety mitigations if deploying.
Intended use
- Norwegian-centric assistant-style tasks (e.g., drafting, summarization, Q&A, light reasoning).
- Assesstment of Norwegian writing style and quality.
- Early evaluation of behavior, language coverage (Norwegian / Bokmål / Nynorsk), and quality.
Limitations
- Preview quality; outputs may be unstable and may hallucinate.
- Not aligned for safety; may follow harmful instructions or generate problematic content (see disclaimer above).
Weights & formats
Transformers (original)
- NbAiLab/borealis-12b-instruct-preview (safetensors).
GGUF quantizations
Available in NbAiLab/borealis-12b-instruct-preview-gguf:
model-q8_0.ggufmodel-f16.ggufmodel-bf16.gguf
Use:
ollama run hf.co/NbAiLab/borealis-12b-instruct-preview-gguf:BF16
MLX (Apple Silicon)
Available in NbAiLab/borealis-12b-instruct-preview-mlx and quantized to 8 bits.
Use:
# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "NbAiLab/borealis-12b-instruct-preview-mlx"
Acknowledgements
Thanks to the Gemma team at Google for releasing Gemma 3 and to everyone contributing feedback on this preview.
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Model tree for NbAiLab/borealis-12b-instruct-preview
Base model
google/gemma-3-12b-pt