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Model: tokhey/Qwen2.5-3B-Egyptian-MCQ Source: Original Platform
2026-08-26 20:27:56 +08:00
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+# 🇪🇬 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.
+
+---
+
+## 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**
+
+---
+# 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 |
+
+---
+
+# 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.
+---
+
+# Output Format
+
+The model always returns structured JSON.
+Example:
+```json
+{
+  "questions": [
+    {
+      "statement": "...",
+      "correct_answer": "...",
+      "plausible_distractors": [
+        "...",
+        "...",
+        "..."
+      ],
+      "explanation": "..."
+    }
+  ]
+}
+```
+
+---
+
+# 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.
+```
+
+---
+
+# 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
+---
+
+# 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 |
+
+---
+
+# How to Use
+## Option 1 — Transformers
+
+Install the required libraries:
+
+```bash
+pip install transformers torch
+```
+
+Load the model:
+
+```python
+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:
+
+```python
+messages = [
+    {
+        "role": "system",
+        "content": SYSTEM_PROMPT
+    },
+    {
+        "role": "user",
+        "content": USER_PROMPT
+    }
+]
+```
+
+Apply the chat template:
+
+```python
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+```
+
+Generate:
+
+```python
+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)
+```
+
+---
+
+## Option 2 — vLLM (Recommended)
+
+Start the server:
+
+```bash
+python -m vllm.entrypoints.openai.api_server \
+    --model tokhey/Qwen2.5-3B-Egyptian-MCQ
+```
+
+Or use Python:
+
+```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:
+
+```python
+outputs = llm.generate(
+    text ,
+    sampling_params,
+)
+
+print(outputs[0].outputs[0].text)
+```
+
+---
+
+# Intended Use
+
+This model is intended for:
+
+- Educational platforms
+- Automatic exam generation
+- AI teaching assistants
+- English assessment generation
+- Curriculum-aware educational systems
+
+---
+
+# Limitations
+
+- Optimized specifically for Egyptian secondary education.
+- Designed for English examination generation.
+- Outputs should be reviewed before being used in official assessments.
+
+---
+
+---
+
+# Tech Stack
+
+- PyTorch
+- Hugging Face Transformers
+- PEFT
+- TRL
+- Unsloth
+- vLLM
+
+---
+
+# 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.