🇪🇬 Egyptian Ministry English MCQ Generator

A fine-tuned Qwen2.5-3B-Instruct model specialized in generating English multiple-choice questions that closely follow the style of the Egyptian Ministry of Education (General Secondary Stage). The model was instruction-tuned using LoRA (PEFT) on a synthetic dataset designed to teach the model the structure, difficulty, and writing style of official Egyptian English examinations.

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Features

• Ministry-style English MCQs
• Grammar question generation
• Vocabulary question generation
• Topic-based generation
• Unit-based generation
• Curriculum-aligned outputs
• JSON-only responses
• One correct answer with three plausible distractors
• Explanation for every generated question
• Optimized for production deployment with vLLM

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Base Model

Property	Value
Base Model	Qwen/Qwen2.5-3B-Instruct
Fine-tuning Method	LoRA (PEFT)
Training Framework	Unsloth + TRL
Inference	Hugging Face Transformers / vLLM
Precision	FP16

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Training Dataset

The model was trained on a synthetic instruction dataset specifically designed for Egyptian Ministry English examinations.

Dataset Statistics

Split	Samples
Training	~5,751
Validation	~640
Total	~6,400

The dataset contains instruction-response conversations covering:

• Grammar
• Vocabulary
• Ministry examination style
• Curriculum units
• Grammar focus
• Topic-based generation
• JSON formatting Instead of memorizing questions, the model learns how official ministry questions are structured.

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Output Format

The model always returns structured JSON. Example:

{
  "questions": [
    {
      "statement": "...",
      "correct_answer": "...",
      "plausible_distractors": [
        "...",
        "...",
        "..."
      ],
      "explanation": "..."
    }
  ]
}

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Example Prompt

System Prompt

You are a Senior English Assessment Specialist and an official item writer for the Egyptian Ministry of Education.
Generate authentic English examination questions.
Return ONLY valid JSON.

User Prompt

Generate 10 grammar questions.

Grammar Focus:
Past Perfect / Past Perfect Continuous

Return only valid JSON.

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Supported Tasks

The model supports requests such as:

• Generate grammar questions
• Generate vocabulary questions
• Generate Unit 7 questions
• Generate questions about "At the Airport"
• Generate Ministry-style MCQs
• Generate questions for a specific grammar focus
• Generate curriculum-based assessments

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Training Configuration

Parameter	Value
Base Model	Qwen2.5-3B-Instruct
LoRA Rank	16
LoRA Alpha	32
LoRA Dropout	0.05
Learning Rate	1e-4
Epochs	2
Optimizer	AdamW 8-bit
Maximum Sequence Length	2048

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How to Use

Option 1 — Transformers

Install the required libraries:

pip install transformers torch

Load the model:

from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL_ID = "tokhey/Qwen2.5-3B-Egyptian-MCQ"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype="auto",
    device_map="auto",
)

Prepare the conversation:

messages = [
    {
        "role": "system",
        "content": SYSTEM_PROMPT
    },
    {
        "role": "user",
        "content": USER_PROMPT
    }
]

Apply the chat template:

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

Generate:

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

outputs = model.generate(
    **inputs,
    max_new_tokens=1200,
    temperature=0.7,
    top_p=0.9,
)

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

print(response)

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Option 2 — vLLM (Recommended)

Start the server:

python -m vllm.entrypoints.openai.api_server \
    --model tokhey/Qwen2.5-3B-Egyptian-MCQ

Or use Python:

from vllm import LLM
from vllm import SamplingParams

llm = LLM(
    model="tokhey/Qwen2.5-3B-Egyptian-MCQ"
)

sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=1200,
)

Generate:

outputs = llm.generate(
    text ,
    sampling_params,
)

print(outputs[0].outputs[0].text)

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Intended Use

This model is intended for:

• Educational platforms
• Automatic exam generation
• AI teaching assistants
• English assessment generation
• Curriculum-aware educational systems

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Limitations

• Optimized specifically for Egyptian secondary education.
• Designed for English examination generation.
• Outputs should be reviewed before being used in official assessments.

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Tech Stack

• PyTorch
• Hugging Face Transformers
• PEFT
• TRL
• Unsloth
• vLLM

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License

This project follows the same license as the base model (Qwen2.5-3B-Instruct). Please refer to the base model repository for licensing details.