Kinyarwanda LoRA Adapter
A LoRA adapter fine-tuned for Kinyarwanda instruction-following, trained on
top of the frozen base model McGill-NLP/AfriqueQwen-4B.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model_name = "McGill-NLP/AfriqueQwen-4B"
adapter_repo = "Leonard250/kinyarwanda-lora-afriqueqwen4b"
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
model = PeftModel.from_pretrained(base_model, adapter_repo)
prompt = "Amabwiriza: Sobanura icyo Irembo ari cyo.\nIgisubizo:"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(output[0], skip_special_tokens=True))
Design notes
- Base model weights are untouched - this adapter only adds trained LoRA matrices (~0.8% of total params).
- Intended for adapter-swapping: attach only when Kinyarwanda is detected; run the base model with no adapter for other languages.
- Trained on a cleaned/filtered subset of
ChrisToukmaji/kinyarwanda_instruction_tuning.
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