Instructions to use Thaka-AI/aisa-arabicfc-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Thaka-AI/aisa-arabicfc-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-12B-it") model = PeftModel.from_pretrained(base_model, "Thaka-AI/aisa-arabicfc-lora") - Notebooks
- Google Colab
- Kaggle
Thaka AISA-ArabicFC adapter
LoRA adapter for the AISA-ArabicFC shared task (ArabicNLP 2026). It fine-tunes
google/gemma-4-12B-it for Arabic function calling: given a user request and a
candidate tool catalog, emit one tool call with arguments, or abstain.
- Base:
google/gemma-4-12B-it - Type: LoRA, rank 64, bfloat16
- Training data: the provided AISA-ArabicFC training split only
Use
Serve with vLLM:
vllm serve google/gemma-4-12B-it \
--enable-lora --max-lora-rank 64 \
--lora-modules primary=./ \
--served-model-name primary --dtype bfloat16 --max-model-len 6144
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