khazarai/Math-VL-8B

Model Description

Base Architecture: Qwen3-VL-8B-Instruct
Fine-Tuning Method: QLoRA (PEFT)
Language: Turkish
Domain: High School Mathematics (12th Grade)
Modality: Vision-Language (Image + Text → Text)

This model is a QLoRA fine-tuned version of Qwen3-VL-8B-Instruct trained on the Turkish-Math-VQA dataset, which consists of 12th-grade mathematics problems published by the Turkish Ministry of National Education (MEB). The model is designed to:

Understand mathematical problem images
Generate step-by-step solutions in Turkish
Handle topics such as logarithms, sequences & series, trigonometry, derivatives, and integrals

Intended Use

Primary Use Cases

Turkish mathematical Visual Question Answering (VQA)
Educational AI assistants
Step-by-step solution generation
Math tutoring systems
Research in Turkish multimodal reasoning

Out-of-Scope Use

Professional exam grading without human validation
Safety-critical mathematical applications
Guaranteed mathematically verified reasoning

Training Data

Dataset: Turkish-Math-VQA The dataset contains mathematics problems from official 12th-grade exams prepared by the Turkish Ministry of National Education.

Dataset Fields:

test_number: The test identifier
question_number: Question number within the test
image: The image containing the math problem
solution: Turkish solution generated synthetically using GPT-o1

Important Note on Labels:

The solution field was generated synthetically by GPT-o1 and has not been manually verified for correctness. While GPT-o1 is generally strong at solving problems at this level, the dataset may contain:

Incorrect reasoning steps
Logical inconsistencies
Arithmetic mistakes

Therefore, the fine-tuned model may inherit these imperfections.

How to Get Started with the Model

from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("khazarai/Math-VL-8B")
model = AutoModelForImageTextToText.from_pretrained("khazarai/Math-VL-8B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "Resimde verilen matematik problemini çözün."}
        ]
    },
]
inputs = processor.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=1024)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Citation

If you use this model in academic work, please cite: