Blame for README.md

---
base_model: google/gemma-4-E4B-it
library_name: peft
license: gemma
tags:
- lora
- peft
- sinhala
- mmlu
---

# Team Sub4 - Sinhala MMLU Shared Task (ICTer 2026): LoRA adapter

LoRA adapter (rank 32, alpha 64) for `google/gemma-4-E4B-it`, fine-tuned on Sinhala school-curriculum multiple-choice questions.
It adapts the attention and MLP projections of the language model only (258 layers, 69.8M trainable parameters).

- **Base model:** `google/gemma-4-E4B-it`, loaded text-only (audio and vision towers removed).
- **Inference parameters:** 7.46B text backbone + 0.07B adapter = 7.53B (limit 8B).
- **Use:** see `inference.py` in the team repository `SinhalaMMLU-SharedTask/team-Sub4`. One forward pass per question, a fixed 4-example prompt, the answer is the highest of the four digit logits ("1".."4").
- **Training:** see `training/` and `README.md` in the team repository. Training data: Sinhala Global MMLU, school past-paper questions, and questions generated from textbook passages with Gemini 3.8 Flash.
- **Stage:** this adapter is the state after continuation stage 2 (stage 1: rows unseen by the first run; stage 2: all extracted and generated questions with shuffled answer positions).

```python
from peft import PeftModel
model = PeftModel.from_pretrained(base_model, "SinhalaMMLU-SharedTask/team-Sub4-gemma4-e4b-lora")
```