Instructions to use khazarai/Math-VL-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use khazarai/Math-VL-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="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": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
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": "What animal is on the candy?"}
        ]
    },
]
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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use khazarai/Math-VL-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "khazarai/Math-VL-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "khazarai/Math-VL-8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/khazarai/Math-VL-8B

SGLang

How to use khazarai/Math-VL-8B 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 "khazarai/Math-VL-8B" \
    --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": "khazarai/Math-VL-8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "khazarai/Math-VL-8B" \
        --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": "khazarai/Math-VL-8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Unsloth Studio new

How to use khazarai/Math-VL-8B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for khazarai/Math-VL-8B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for khazarai/Math-VL-8B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for khazarai/Math-VL-8B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="khazarai/Math-VL-8B",
    max_seq_length=2048,
)

Docker Model Runner
How to use khazarai/Math-VL-8B with Docker Model Runner:
```
docker model run hf.co/khazarai/Math-VL-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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:

The original Qwen model
Turkish-Math-VQA dataset

Downloads last month: 12

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for khazarai/Math-VL-8B

Base model

Qwen/Qwen3-VL-8B-Instruct

Finetuned

unsloth/Qwen3-VL-8B-Instruct

Finetuned

(108)

this model

Quantizations

1 model

khazarai
/

Math-VL-8B