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Model: tokhey/question-generator-model-qwen2.5-1.5b
Source: Original Platform

README.md

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+---
+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
+
+---