Instructions to use tokhey/question-generator-model-qwen2.5-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tokhey/question-generator-model-qwen2.5-1.5b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tokhey/question-generator-model-qwen2.5-1.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tokhey/question-generator-model-qwen2.5-1.5b")
model = AutoModelForCausalLM.from_pretrained("tokhey/question-generator-model-qwen2.5-1.5b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tokhey/question-generator-model-qwen2.5-1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tokhey/question-generator-model-qwen2.5-1.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tokhey/question-generator-model-qwen2.5-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tokhey/question-generator-model-qwen2.5-1.5b

SGLang

How to use tokhey/question-generator-model-qwen2.5-1.5b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tokhey/question-generator-model-qwen2.5-1.5b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tokhey/question-generator-model-qwen2.5-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tokhey/question-generator-model-qwen2.5-1.5b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tokhey/question-generator-model-qwen2.5-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tokhey/question-generator-model-qwen2.5-1.5b with Docker Model Runner:
```
docker model run hf.co/tokhey/question-generator-model-qwen2.5-1.5b
```

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

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

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:

do_sample=False

For more diverse generations:

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

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for tokhey/question-generator-model-qwen2.5-1.5b

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Adapter

(1285)

this model