README.md

---
license: cc-by-nc-4.0
language:
- ko
base_model:
- google/gemma-3-4b-it
pipeline_tag: text-generation
library_name: transformers
tags:
- exam
- question-generation
- gemma-3
- korean
- xml
- sft
- dpo
- grpo
---

# Gemma3 ExamGen (Korean, XML)

**TL;DR**: A Gemma-3–based model fine-tuned to generate **Korean** university-level exam questions in **strict XML** (5 problems: 2 MCQ, 2 short-answer, 1 essay).  
> **Outputs are in Korean.**

---

## Overview
Gemma3 ExamGen is a fine-tuned variant of Gemma-3 designed to generate Korean university exam questions in a strict XML structure.  
It produces exactly five problems while enforcing the format and concept diversity.

---

## Intended Use
- **Primary** : Generate Korean exam problems in XML.  
- **Output Language** : Korean only.  
- **Not for** : factual certification, grading, or unreviewed deployment.

---

## Training Pipeline
- **Base** : `google/gemma-3-4b-it`  
- **Stages** : SFT → DPO → GRPO  
- **Method** : LoRA fine-tuning  
- **Data** : PDF-crawled educational materials (private)  
- **Filtering** : ensured XML validity and unique concepts.

---

## Prompting Spec (Korean Prompt Template)

> The model must always produce **Korean outputs**.  
> It strictly follows the XML schema and rules defined below.  
> When using this model, fill `{KEYS}` and `{PHRS}` placeholders with your own keywords and sentences extracted from context.

---

### Prompt Template (in Korean)

```text
다음의 규칙을 준수하여 대학교 시험 문제 5개를 XML 형식으로 생성하세요.

**응답 형식 (반드시 준수):**
<problems>
    <problem>
        <number>1</number>
        <type>객관식</type>
        <content>문제내용</content>
        <description>풀이과정</description>
        <answer>답</answer>
    </problem>
    <problem>
        <number>2</number>
        <type>객관식</type>
        <content>문제내용</content>
        <description>풀이과정</description>
        <answer>답</answer>
    </problem>
    
    <problem>
        <number>3</number>
        <type>단답형</type>
        <content>문제내용</content>
        <description>풀이과정</description>
        <answer>답</answer>
    </problem>
    <problem>
        <number>4</number>
        <type>단답형</type>
        <content>문제내용</content>
        <description>풀이과정</description>
        <answer>답</answer>
    </problem>
    
    <problem>
        <number>5</number>
        <type>주관식</type>
        <content>문제내용</content>
        <answer>답</answer>
    </problem>
</problems>

**절대 규칙 (위반 시 응답 무효):**
1. XML 태그 구조만 출력합니다. 다른 텍스트, 설명, 주석은 포함하지 않습니다.
2. 모든 내용은 CDATA 섹션 없이 일반 텍스트로 작성합니다.
3. 특수문자는 XML 엔티티로 작성합니다. (&lt;, &gt;, &amp;, &quot;, &apos;)

**문제 생성 규칙:**
- 총 5문제를 생성하며, 문제 유형은 다음 비율을 반드시 지킵니다: 객관식 2문제, 단답형 2문제, 주관식 1문제.
- 각 문제의 <type>은 위 응답 형식에서 이미 지정된 값을 그대로 사용합니다.
- 객관식 문제는 보기 기호를 ①, ②, ③, ④, ⑤ 형식으로 작성합니다.
- 모든 문제는 서로 다른 주요 개념을 사용해야 하며, 동일 개념이나 동일 인물, 동일 사건을 다른 문제에서 재사용하지 않습니다.
- 풀이과정과 답을 구체적으로 작성합니다.
- 문제 내용에 따옴표, 수식, 특수문자 등을 자유롭게 사용할 수 있습니다.
- 문제는 난이도와 표현 방식을 다양하게 구성합니다.

**중요한 키워드:**
{KEYS}
**중요한 문장들:**
{PHRS}
```

---

## Example Usage

```python
from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

model_id = "yongjin-KIM/gemma3-examgen"
model = AutoModelForImageTextToText.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
processor = AutoProcessor.from_pretrained(model_id)
tok = processor.tokenizer

prompt = """<Insert the Korean prompt template here and replace {KEYS} and {PHRS}>"""

inputs = tok(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=2000,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
)
print(tok.decode(outputs[0], skip_special_tokens=True))
```

---

## Output Format Guarantees

- Always produces **well-formed XML**.  
- Exactly **5 `<problem>` blocks**.  
- Escapes all special characters (`&lt;`, `&gt;`, `&amp;`, `&quot;`, `&apos;`).  
- Fixed type order:  
  **객관식**, **객관식**, **단답형**, **단답형**, **주관식**.

---


## Limitations

- May occasionally omit `<description>` fields or produce overlong answers.  
- Factual correctness is not guaranteed.  
- Designed for **Korean text only**; English prompts are not supported.  
- Contextual consistency may vary depending on {KEYS}/{PHRS} quality.

---

## Ethical Considerations

- Intended for educational and research use only.  
- Should not be used for unsupervised or high-stakes exam generation.  
- All generated content should be **reviewed by a human instructor** before use.

---

## Model Details

- **Base Model**: `google/gemma-3-4b-it`  
- **Architecture**: Decoder-only transformer  
- **Fine-tuning Method**: LoRA (r=8, α=32)  
- **Training Framework**: PEFT + TRL  
- **Training Hardware**: 2 × A100 (80GB)  
- **Training Duration**: ~48 hours  
- **Stages**: SFT → DPO → GRPO  

---

## License

- **Model**: CC-BY-NC-4.0  
- **Base Model**: Gemma-3 (Google)  
- **Dataset**: Private (PDF-crawled educational material)  
- **Intended Use**: Research / Non-commercial

---

## Maintainer

**Author:** Yongjin Kim  
**Hugging Face:** [@yongjin-KIM](https://huggingface.co/yongjin-KIM)

---