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Model: ai-for-good-lab/byol-mri-12b-it
Source: Original Platform

README.md

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+---
+license: gemma
+language:
+  - mi
+  - en
+base_model: google/gemma-3-12b-pt
+tags:
+  - byol
+  - low-resource
+  - māori
+  - gemma3
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# BYOL Māori 12B IT
+
+This model was produced by the [BYOL framework](https://github.com/microsoft/byol)
+for extending LLMs to low-resource languages.
+
+- **Base model:** [google/gemma-3-12b-pt](https://huggingface.co/google/gemma-3-12b-pt)
+- **Language:** Māori (mri)
+- **Training stage:** Instruction Tuning (SFT)
+- **License:** [Gemma Terms of Use](https://ai.google.dev/gemma/terms) (derived from Gemma 3)
+- **Paper:** [BYOL: Bring Your Own Language Into LLMs](https://arxiv.org/abs/2601.10804)
+- **Code:** [github.com/microsoft/byol](https://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](https://huggingface.co/ai-for-good-lab/byol-mri-12b-cpt) checkpoint, using translated instruction-following data (SmolTalk2 + AYA) generated via the [BYOL framework](https://github.com/microsoft/byol).
+
+This is an intermediate checkpoint used to produce the merged model. For best results, use the [merged variant](https://huggingface.co/ai-for-good-lab/byol-mri-12b-merged) instead, which combines the language knowledge from CPT with the instruction-following ability from this model.
+
+## Usage
+
+```bash
+pip install -U transformers
+```
+
+```python
+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
+
+```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},
+}
+```