ModelHub XC 02099b7a4d 初始化项目,由ModelHub XC社区提供模型
Model: ai-for-good-lab/byol-mri-12b-it
Source: Original Platform
2026-06-25 11:54:16 +08:00

license, language, base_model, tags, library_name, pipeline_tag
license language base_model tags library_name pipeline_tag
gemma
mi
en
google/gemma-3-12b-pt
byol
low-resource
māori
gemma3
transformers text-generation

BYOL Māori 12B IT

This model was produced by the BYOL framework for extending LLMs to low-resource languages.

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
Model synced from source: ai-for-good-lab/byol-mri-12b-it
Readme 74 KiB