Instructions to use salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter with Transformers:

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

pipe = pipeline("image-text-to-text", model="salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter")
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 AutoModel
model = AutoModel.from_pretrained("salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter",
		"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/salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter

SGLang

How to use salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter 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 "salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter" \
    --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": "salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter",
		"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 "salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter" \
        --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": "salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter with Docker Model Runner:
```
docker model run hf.co/salihfurkaan/Qwen2.5-VL-ScienceQA-Math-CoT-Adapter
```

Qwen2.5-VL-3B-ScienceQA-Math-CoT

A LoRA fine-tuned version of Qwen2.5-VL-3B-Instruct for mathematical reasoning and multimodal question answering using the ScienceQA-Math-CoT dataset.

The model is designed to solve image-based and text-based mathematics problems through step-by-step reasoning while improving instruction-following capabilities in both Turkish and English.

Model Description

This model was fine-tuned from Qwen2.5-VL-3B-Instruct using Parameter-Efficient Fine-Tuning (LoRA).

The training objective focused on:

Mathematical reasoning
Chain-of-thought style explanations
Visual question answering
ScienceQA mathematical problems
Multimodal image-text understanding
Turkish and English instruction following

Base Model

Qwen2.5-VL-3B-Instruct

Fine-Tuning Method

QLoRA (4-bit)
LoRA adapters
PEFT
Hugging Face Transformers

Training Dataset

ScienceQA-Math-CoT

The dataset contains:

ScienceQA mathematical questions
Associated images
Step-by-step solutions
Final answers

The model was trained to generate reasoning traces before producing final answers.

Intended Uses

Suitable Uses

Educational assistants
Mathematical tutoring
Visual mathematical reasoning
STEM learning applications
Homework support
Science question answering
Turkish and English multimodal assistants

Out-of-Scope Uses

This model is not intended for:

Medical diagnosis
Legal advice
Financial decision-making
Safety-critical systems
Autonomous decision-making

Training Details

Hardware

Kaggle Dual NVIDIA T4 GPUs

Training Configuration

Parameter	Value
Base Model	Qwen2.5-VL-3B-Instruct
Fine-Tuning	LoRA
Quantization	4-bit NF4
Precision	FP16
LoRA Rank	16
LoRA Alpha	32
Learning Rate	2e-4
Batch Size	1
Gradient Accumulation	4
Epochs	1

Example Usage

from transformers import Qwen2_5_VLForConditionalGeneration
from transformers import AutoProcessor
from peft import PeftModel

base_model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2.5-VL-3B-Instruct",
    device_map="auto"
)

model = PeftModel.from_pretrained(
    base_model,
    "salihfurkaan/Qwen2.5-VL-3B-ScienceQA-Math-CoT-Adapter"
)

processor = AutoProcessor.from_pretrained(
    "Qwen/Qwen2.5-VL-3B-Instruct"
)

Limitations

The model may still produce incorrect mathematical reasoning.
Chain-of-thought outputs do not guarantee correctness.
Performance depends on image quality and clarity.
The model has not been evaluated on all mathematical domains.
The model may hallucinate intermediate reasoning steps.

Ethical Considerations

This model is intended for educational and research purposes.
Users should independently verify mathematical solutions before relying on them in academic, professional, or real-world settings.

Citation

@misc{qwen2vl_scienceqa_math_cot, title={Qwen2.5-VL-3B-ScienceQA-Math-CoT}, author={Salih Furkan Erik, Kerem Berke Başak}, year={2026}, publisher={Hugging Face}, howpublished={\url{https://huggingface.co/}} }

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