SinhalaMMLU-SharedTask/team-nena

Team Nena – Sinhala MMLU Shared Task System

System summary

• Base model(s): SAWithanage/SinLlama-Llama-3-8B-Merged
• What we did: We performed parameter-efficient fine-tuning (LoRA) of SinLlama-8B on authentic Sri Lankan national curriculum examination Multiple-Choice Questions (spanning Grades 6–13 across 14 official subjects) alongside domain-adaptive curriculum pretraining passages. For inference, we employ a Sinhala Micro-Chain-of-Thought (Micro-CoT) reasoning prompt with deterministic greedy decoding to ground curriculum facts before emitting the final anchored choice.
• Total inference parameters: 7.954B parameters (Language-based vocabulary trimming (Lang-Trim) slices out 20,336 inactive foreign script tokens while preserving 100% of Sinhala, English, numbers, and symbols, safely conforming to the Hard Rule 02 parameter ceiling of 8.00B as officially approved by the shared task chairs).
• Extra data used for training or retrieval: Curated Sri Lankan national curriculum examination MCQs and government textbook passages across Grades 6–13. Strictly retrieval-free at inference time (zero external APIs, web search, or RAG).

Models and resources in this submission

Repo	What it is
SinhalaMMLU-SharedTask/team-nena	Code, inference script, final predictions, and training scripts (this repo)
SinhalaMMLU-SharedTask/team-nena-sinllama-8b-lora	Fine-tuned LoRA adapter weights and tokenizer configuration

How to run

pip install -r requirements.txt
python inference.py

• GPU used: 1x Nvidia A100 (80GB / 40GB) or 1x T4 / RTX 3090 / 4090 (with 4-bit NF4)
• Approximate run time on the test set: ~80–90 minutes for 2,238 test questions
• Anything else the organizers need to know: Pure offline inference, zero closed or paid APIs.

Files

• inference.py – the exact script that produced our final Codabench submission
• predictions.jsonl – the exact file we submitted to the final leaderboard (2,238 predictions)
• requirements.txt – package versions used
• training/ – training, fine-tuning, and data-preparation code and configurations