This is a continually pre-trained (CPT) language model adapted for Māori (mri). Starting from Gemma 3 1b, the model was further trained on a curated bilingual corpus of Māori and English text using the BYOL framework. This extends the base model's knowledge and fluency in Māori while retaining its English capabilities.

As a base (non-instruction-tuned) model, it is best suited for text completion tasks.

Usage

Base model: <a href="https://huggingface.co/google/gemma-3-1b-pt" target="_blank" rel="noopener noreferrer" class="text-blue-400 hover:text-blue-300 underline underline-offset-2 transition-colors">google/gemma-3-1b-pt
Language: Māori (mri)
Training stage: Continual Pre-Training
License: <a href="https://ai.google.dev/gemma/terms" target="_blank" rel="noopener noreferrer" class="text-blue-400 hover:text-blue-300 underline underline-offset-2 transition-colors">Gemma Terms of Use (derived from Gemma 3)
Paper: <a href="https://arxiv.org/abs/2601.10804" target="_blank" rel="noopener noreferrer" class="text-blue-400 hover:text-blue-300 underline underline-offset-2 transition-colors">BYOL: Bring Your Own Language Into LLMs
Code: <a href="https://github.com/microsoft/byol" target="_blank" rel="noopener noreferrer" class="text-blue-400 hover:text-blue-300 underline underline-offset-2 transition-colors">github.com/microsoft/byol

pip install -U transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ai-for-good-lab/byol-mri-1b-cpt"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", dtype=torch.bfloat16)

# Text completion (base model)
prompt = "Ko Aotearoa he whenua"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citation

bibtex

@article{zamir2026byolbringlanguagellms,
    title={BYOL: Bring Your Own Language Into LLMs},
    author={Syed Waqas Zamir and Wassim Hamidouche and Boulbaba Ben Amor and Luana Marotti and Inbal Becker-Reshef and Juan Lavista Ferres},
    year={2026},
    journal={arXiv:2601.10804},
    url={https://arxiv.org/abs/2601.10804},
}