---
language:
- en
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- education
- question-generation
- qwen2.5
- lora
- egypt
- english
- mcq
- json
---

# Egyptian Ministry English Exam Question Generator (Qwen2.5-1.5B)

This model is a fine-tuned version of **Qwen2.5-1.5B-Instruct** designed to generate English multiple-choice questions that closely resemble the official examinations of the **Egyptian Ministry of Education (General Secondary Stage)**.

Unlike generic question generation models, this model was trained specifically to reproduce the wording, structure, difficulty, distractor quality, and examination style commonly found in Egyptian Ministry exams.

---

# Overview

The model receives curriculum information (Unit, Topic, Grammar Focus, Target Skills, Life Skills, Core Values) together with a user request and generates Ministry-style English MCQs in structured JSON format.

The objective is to assess **understanding rather than memorization**, while preserving the writing style used in official examinations.

---

# Base Model

- **Model:** Qwen/Qwen2.5-1.5B-Instruct
- **Fine-tuning:** LoRA
- **Framework:** LLaMA-Factory
- **Training Type:** Supervised Fine-Tuning (SFT)
- **Deployment Model:** Merged LoRA + Base Model

---

# Dataset

The model was trained using two complementary datasets.

## 1. Official Egyptian Ministry Dataset

A manually collected dataset extracted from previous official English examinations.

Each sample was converted into an instruction-following format including:

- curriculum metadata
- grammar focus
- target skills
- ministry-style output

---

## 2. Synthetic Dataset

To improve generalization, an additional synthetic dataset was generated using **GLM-5.2**.

The synthetic data follows the same curriculum structure and examination philosophy while introducing diverse contexts and wording.

This significantly increased dataset diversity without changing the Ministry examination style.

---

# Capabilities

The model can generate:

- Grammar Questions
- Vocabulary Questions
- Reading Questions
- Writing Questions
- Ministry-style Distractors
- JSON Responses
- Multiple Questions in a Single Request

Each generated question contains:

- statement
- correct_answer
- plausible_distractors
- explanation

---

# Recommended Prompt Structure

Although the model understands simple prompts, it performs significantly better when curriculum information is supplied.

Recommended prompt:

- User request
- Unit information
- Topic
- Grammar Focus
- Target Skills
- Life Skills
- Core Values
- JSON Output Schema

---

# Example Prompt

Generate 5 Ministry-style grammar MCQs.

Unit: 11

Topic:
Literature

Grammar Focus:
Comparative and Superlative Adjectives

Return JSON only.

---

# Loading the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "tokhey/question-generator-model-qwen2.5-1.5b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto"
)
```

---

# Example Inference

```python
messages = [
    {
        "role":"user",
        "content":"""
Generate 3 Ministry-style grammar MCQs.

Unit: 8

Topic:
A Journey Through Time

Grammar Focus:
Active and Passive Voice

Return JSON only.
"""
    }
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    do_sample=False
)

print(
    tokenizer.decode(
        outputs[0][inputs.input_ids.shape[-1]:],
        skip_special_tokens=True
    )
)
```

---

# Inference Tips

For deterministic evaluation:

```python
do_sample=False
```

For more diverse generations:

```python
do_sample=True
temperature=0.7
top_p=0.9
```

---

# Intended Applications

- Educational Platforms
- AI Tutors
- Question Generation Systems
- Ministry-style Practice Exams
- Curriculum Assessment Systems
- Intelligent Learning Platforms

---

# Limitations

- The model is optimized for Egyptian Ministry English examinations.
- Best performance is achieved when curriculum information is provided.
- Output quality depends on prompt specificity.

---

# Acknowledgements

This model was developed as part of an undergraduate graduation project focused on automatic generation of Egyptian Ministry-style English examination questions using Large Language Models.

Base Model:
Qwen Team

Fine-tuning Framework:
LLaMA-Factory

Synthetic Data Generation:
GLM-5.2

---