Instructions to use Bombek1/slide2svg-vl-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bombek1/slide2svg-vl-2b with Transformers:

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

pipe = pipeline("image-text-to-text", model="Bombek1/slide2svg-vl-2b")
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("Bombek1/slide2svg-vl-2b", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Bombek1/slide2svg-vl-2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bombek1/slide2svg-vl-2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bombek1/slide2svg-vl-2b",
		"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/Bombek1/slide2svg-vl-2b

SGLang

How to use Bombek1/slide2svg-vl-2b 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 "Bombek1/slide2svg-vl-2b" \
    --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": "Bombek1/slide2svg-vl-2b",
		"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 "Bombek1/slide2svg-vl-2b" \
        --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": "Bombek1/slide2svg-vl-2b",
		"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 Bombek1/slide2svg-vl-2b 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 Bombek1/slide2svg-vl-2b 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 Bombek1/slide2svg-vl-2b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Bombek1/slide2svg-vl-2b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Bombek1/slide2svg-vl-2b",
    max_seq_length=2048,
)

Docker Model Runner
How to use Bombek1/slide2svg-vl-2b with Docker Model Runner:
```
docker model run hf.co/Bombek1/slide2svg-vl-2b
```

slide2svg-vl-2b

A vision-language model fine-tuned to convert presentation slides (images) into structured SVG format.

Model Description

This model takes an image of a presentation slide and generates corresponding SVG markup, preserving:

Text content and positioning
Font styling (family, size, weight, color)
Image placements
Layout structure

Training Details

Parameter	Value
Base Model	Qwen3-VL-2B-Instruct
Dataset	ahazimeh/slide2svg (~39,500 samples)
Method	LoRA (r=64, alpha=128)
Epochs	2
Batch Size	16 (2 × 8 gradient accumulation)
Learning Rate	1e-4
Context Length	12,288 tokens
Precision	16-bit
Training Time	~5.5 hours
Hardware	NVIDIA RTX 5090 (32GB)

Evaluation Results

Metric	Score
Valid XML Structure	100% (20/20)
Has `<svg>` tag	100% (20/20)
Has `<foreignObject>`	100% (20/20)
Final Training Loss	0.091
Final Validation Loss	0.091
Avg Output Length	91% of reference

Usage

With Unsloth

from unsloth import FastVisionModel
from PIL import Image

# Load model
model, tokenizer = FastVisionModel.from_pretrained(
    "Bombek1/slide2svg-vl-2b",
    load_in_4bit=True,  # or False for 16-bit
)
FastVisionModel.for_inference(model)

# Prepare input
image = Image.open("your_slide.png")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": "Convert this presentation slide to SVG format."}
        ]
    }
]

# Generate
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=4096, do_sample=False)
generated_ids = outputs[0][inputs["input_ids"].shape[1]:]
svg_output = tokenizer.decode(generated_ids, skip_special_tokens=True)

print(svg_output)

Output Format

The model generates SVG with the following structure:

<?xml version="1.0" encoding="utf-8"?>
<html>
<svg xmlns="http://www.w3.org/2000/svg" width="1024" height="768" fill="white">
  <image x="0.0%" y="0.0%" width="100.0%" href="background.png" />
  <g id="images">
    <image x="14.3%" y="28.5%" width="35.8%" href="image_0.png" />
  </g>
  <g id="text">
    <foreignObject x="5.4%" y="8.1%" width="32.0%" height="12.0%" overflow="visible">
      <div xmlns="http://www.w3.org/1999/xhtml" style="font-family: Inter; font-size: 74px; font-weight: bold; color: #000000;">
        <div>Your Text Here</div>
      </div>
    </foreignObject>
  </g>
</svg>
</html>

Limitations

Outputs are ~91% the length of reference SVGs (may omit some minor elements)
Default output resolution is 1024×768 (positions use percentages, so they scale)
Best results on slides similar to the training data (presentation-style layouts)
Complex diagrams or charts may not be fully captured

License

Apache 2.0 (inherited from Qwen3-VL base model)

Acknowledgments

Base model: Qwen3-VL-2B-Instruct by Alibaba
Dataset: ahazimeh/slide2svg
Training framework: Unsloth

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for Bombek1/slide2svg-vl-2b

Base model

Qwen/Qwen3-VL-2B-Instruct

Finetuned

unsloth/Qwen3-VL-2B-Instruct

Adapter

(3)

this model

Bombek1
/

slide2svg-vl-2b