metadata
license: gemma
datasets:
- NbAiLab/aurora-sft-2512-filtered
language:
- 'no'
- nb
- nn
base_model: NbAiLab/borealis-4b-instruct-preview
pipeline_tag: text-generation
library_name: mlx
tags:
- conversational
- instruct
- experimental
- mlx
Borealis 4B Instruct MLX (Preview)
Release: Dec 22nd, 2025.
Model summary
NbAiLab/borealis-4b-instruct-preview-mlx is a MLX version of 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.
The original model is NbAiLab/borealis-4b-instruct-preview.
| Model | Bits | Format |
|---|---|---|
| NbAiLab/borealis-4b-instruct-preview | BF16 | Transformers (safetensors) |
| NbAiLab/borealis-4b-instruct-preview-gguf | 8 | GGUF (q8_0) |
| NbAiLab/borealis-4b-instruct-preview-gguf | 16 | GGUF (f16) |
| NbAiLab/borealis-4b-instruct-preview-gguf | BF16 | GGUF (bf16) |
| NbAiLab/borealis-4b-instruct-preview-mlx | 32 | MLX |
| NbAiLab/borealis-4b-instruct-preview-mlx-8bits | 8 | MLX (quantized) |
This model NbAiLab/borealis-4b-instruct-preview-mlx was converted to MLX format from NbAiLab/borealis-4b-instruct-preview using mlx-lm version 0.29.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("NbAiLab/borealis-4b-instruct-preview-mlx")
prompt = "hei :)"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)