02099b7a4d80481c003893a6b8abcddccc72b6f4
Model: ai-for-good-lab/byol-mri-12b-it Source: Original Platform
license, language, base_model, tags, library_name, pipeline_tag
| license | language | base_model | tags | library_name | pipeline_tag | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| gemma |
|
google/gemma-3-12b-pt |
|
transformers | text-generation |
BYOL Māori 12B IT
This model was produced by the BYOL framework for extending LLMs to low-resource languages.
- Base model: google/gemma-3-12b-pt
- Language: Māori (mri)
- Training stage: Instruction Tuning (SFT)
- License: Gemma Terms of Use (derived from Gemma 3)
- Paper: BYOL: Bring Your Own Language Into LLMs
- Code: github.com/microsoft/byol
Model Description
This is an instruction-tuned (SFT) language model for Māori (mri). It was created by applying supervised fine-tuning on top of the BYOL Māori 12b CPT checkpoint, using translated instruction-following data (SmolTalk2 + AYA) generated via the BYOL framework.
This is an intermediate checkpoint used to produce the merged model. For best results, use the merged variant instead, which combines the language knowledge from CPT with the instruction-following ability from this model.
Usage
pip install -U transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "ai-for-good-lab/byol-mri-12b-it"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", dtype=torch.bfloat16)
# Chat inference
messages = [{"role": "user", "content": "Kōrerotia mai mō Aotearoa."}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True, return_dict=True).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Citation
@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},
}
Description