Instructions to use ai-for-good-lab/byol-mri-12b-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ai-for-good-lab/byol-mri-12b-merged with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ai-for-good-lab/byol-mri-12b-merged")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("ai-for-good-lab/byol-mri-12b-merged")
model = AutoModelForImageTextToText.from_pretrained("ai-for-good-lab/byol-mri-12b-merged")
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

vLLM

How to use ai-for-good-lab/byol-mri-12b-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ai-for-good-lab/byol-mri-12b-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ai-for-good-lab/byol-mri-12b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ai-for-good-lab/byol-mri-12b-merged

SGLang

How to use ai-for-good-lab/byol-mri-12b-merged 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 "ai-for-good-lab/byol-mri-12b-merged" \
    --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": "ai-for-good-lab/byol-mri-12b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ai-for-good-lab/byol-mri-12b-merged" \
        --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": "ai-for-good-lab/byol-mri-12b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ai-for-good-lab/byol-mri-12b-merged with Docker Model Runner:
```
docker model run hf.co/ai-for-good-lab/byol-mri-12b-merged
```

BYOL Māori 12B

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: Merged (CPT + IT via model merging)
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 a merged language model for Māori (mri) that combines the language knowledge acquired during continual pre-training with the instruction-following capabilities from supervised fine-tuning. It was produced by merging BYOL Māori 12b CPT and BYOL Māori 12b IT checkpoints back into the original Gemma 3 instruction model, using the BYOL framework.

This is the recommended model for most users. It supports chat/instruction-following and has the strongest overall performance on Māori benchmarks (see the paper for evaluation results).

Usage

pip install -U transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ai-for-good-lab/byol-mri-12b-merged"
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},
}