Image-Text-to-Text
Transformers
Safetensors
Norwegian
Norwegian Bokmål
Norwegian Nynorsk
gemma3
conversational
instruct
experimental
text-generation-inference
🇪🇺 Region: EU
Instructions to use NbAiLab/borealis-4b-instruct-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/borealis-4b-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-4b-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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("NbAiLab/borealis-4b-instruct-preview") model = AutoModelForMultimodalLM.from_pretrained("NbAiLab/borealis-4b-instruct-preview", device_map="auto") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NbAiLab/borealis-4b-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-4b-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-4b-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-4b-instruct-preview
- SGLang
How to use NbAiLab/borealis-4b-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-4b-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-4b-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-4b-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-4b-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-4b-instruct-preview with Docker Model Runner:
docker model run hf.co/NbAiLab/borealis-4b-instruct-preview
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tags:
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- instruct
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tags:
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- conversational
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- instruct
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- experimental
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---
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# Borealis 4B Instruct (Preview)
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## Model summary
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**NbAiLab/borealis-4b-instruct-preview** is a **4B-parameter** instruction-tuned **preview** model intended for early testing and feedback. It is an **experiment** and should be treated as pre-release quality.
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This model is based on [**google/gemma-3-4b-it**](https://huggingface.co/google/gemma-3-4b-it), and fine-tuned on textual instructions only.
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## Training data
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Supervised fine-tuning (SFT) uses **NbAiLab/aurora-sft-2512** (not released yet).
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## ⚠️ Safety / alignment disclaimer (important)
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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.
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## Intended use
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- Norwegian-centric assistant-style tasks (e.g., drafting, summarization, Q&A, light reasoning).
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- Assesstment of Norwegian writing style and quality.
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- Early evaluation of behavior, language coverage (Norwegian / Bokmål / Nynorsk), and quality.
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## Limitations
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- Preview quality; outputs may be unstable and may hallucinate.
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- Not aligned for safety; may follow harmful instructions or generate problematic content (see disclaimer above).
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## Weights & formats
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### Transformers (original)
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- **NbAiLab/borealis-4b-instruct-preview** (safetensors).
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### GGUF quantizations
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Available in [**NbAiLab/borealis-4b-instruct-preview-gguf**](https://huggingface.co/NbAiLab/borealis-4b-instruct-preview-gguf):
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- `model-q8_0.gguf`
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- `model-f16.gguf`
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- `model-bf16.gguf`
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### MLX (Apple Silicon)
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Available in [**NbAiLab/borealis-4b-instruct-preview-mlx**](https://huggingface.co/NbAiLab/borealis-4b-instruct-preview-mlx)
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## Acknowledgements
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Thanks to the **Gemma** team at Google for releasing Gemma 3 and to everyone contributing feedback on this preview.
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