Instructions to use mclpio/uta-sensei-tiny-aya-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mclpio/uta-sensei-tiny-aya-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("CohereLabs/tiny-aya-global") model = PeftModel.from_pretrained(base_model, "mclpio/uta-sensei-tiny-aya-lora") - Notebooks
- Google Colab
- Kaggle
Uta Sensei Tiny Aya (adapter)
Task-specific Uta Sensei fine-tune of CohereLabs/tiny-aya-global for Japanese lyric lesson
annotation and grounded tutoring. Training run: 20260614T202728Z-full.
The model inherits Tiny Aya Global's CC-BY-NC 4.0 license and acceptable-use requirements. See the Uta Sensei repository for dataset design, benchmark results, and training configuration.
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