# 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
```bash
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