Instructions to use vinoku89/svg-code-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vinoku89/svg-code-generator with Transformers:

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

pipe = pipeline("text-generation", model="vinoku89/svg-code-generator")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vinoku89/svg-code-generator")
model = AutoModelForCausalLM.from_pretrained("vinoku89/svg-code-generator", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vinoku89/svg-code-generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vinoku89/svg-code-generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vinoku89/svg-code-generator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vinoku89/svg-code-generator

SGLang

How to use vinoku89/svg-code-generator 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 "vinoku89/svg-code-generator" \
    --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": "vinoku89/svg-code-generator",
		"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 "vinoku89/svg-code-generator" \
        --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": "vinoku89/svg-code-generator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vinoku89/svg-code-generator with Docker Model Runner:
```
docker model run hf.co/vinoku89/svg-code-generator
```

vinoku89 commited on Jul 26, 2025

Commit

3621384

verified ·

1 Parent(s): 4227547

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +7 -51

README.md CHANGED Viewed

@@ -1,23 +1,16 @@
 ---
 license: apache-2.0
-base_model: microsoft/DialoGPT-medium
 tags:
 - code-generation
 - svg
 - lora
 - fine-tuned
 - peft
-- graphics
-- art
 language:
 - en
 pipeline_tag: text-generation
-library_name: peft
-datasets:
-- custom-svg-dataset
-metrics:
-- bleu
-- rouge
 model_type: lora
 inference: true
 widget:
@@ -27,19 +20,6 @@ widget:
   text: "Draw a blue rectangle with black border"
 - example_title: "Complex Shape"
   text: "Generate a star with 5 points in yellow"
-model-index:
-- name: svg-code-generator
-  results:
-  - task:
-      type: text-generation
-      name: SVG Code Generation
-    metrics:
-    - type: bleu
-      value: 0.85
-      name: BLEU Score
-    - type: rouge
-      value: 0.78
-      name: ROUGE Score
 ---
 # SVG Code Generator
@@ -49,12 +29,11 @@ This is a fine-tuned LoRA adapter for generating SVG code from natural language
 ## Model Details
 - **Model Name**: model_v10
-- **Base Model**: Fine-tuned language model
 - **Training Method**: LoRA (Low-Rank Adaptation)
 - **Task**: Text-to-SVG code generation
 - **Model Type**: Causal Language Model with LoRA adapter
-- **Parameters**: ~7B (base) + LoRA parameters
-- **Training Framework**: PyTorch with PEFT
 ## Usage
@@ -62,41 +41,18 @@ Load the model using the transformers library and PEFT for LoRA adapters. Use na
 ## Training Data
-The model was trained on SVG code generation tasks with natural language descriptions covering:
-- Basic shapes (circles, rectangles, polygons)
-- Complex graphics and patterns
-- Color specifications and styling
-- Positioning and sizing instructions
 ## Intended Use
-This model is designed to generate SVG code from text descriptions for:
-- Educational purposes
-- Creative projects
-- Rapid prototyping of graphics
-- Learning SVG syntax
 ## Limitations
 - Generated SVG may require validation
 - Performance depends on prompt clarity
 - Limited to SVG syntax and features seen during training
-- May not handle very complex geometric calculations
 ## Model Performance
-The model has been fine-tuned specifically for SVG generation tasks and achieves:
-- BLEU Score: 0.85
-- ROUGE Score: 0.78
-- High accuracy on basic shapes and common patterns
-## Citation
-If you use this model, please cite:
-@misc{svg-code-generator,
-title={SVG Code Generator},
-author={Your Name},
-year={2025},
-publisher={HuggingFace},
-url={https://huggingface.co/your_username/svg-code-generator}
-}

 ---
 license: apache-2.0
+base_model: qwen3-0.6B
 tags:
 - code-generation
 - svg
 - lora
 - fine-tuned
 - peft
 language:
 - en
 pipeline_tag: text-generation
+library_name: transformers
 model_type: lora
 inference: true
 widget:
   text: "Draw a blue rectangle with black border"
 - example_title: "Complex Shape"
   text: "Generate a star with 5 points in yellow"
 ---
 # SVG Code Generator
 ## Model Details
 - **Model Name**: model_v10
+- **Base Model**: qwen3-0.6B
 - **Training Method**: LoRA (Low-Rank Adaptation)
 - **Task**: Text-to-SVG code generation
 - **Model Type**: Causal Language Model with LoRA adapter
+- **Library**: Transformers
 ## Usage
 ## Training Data
+The model was trained on SVG code generation tasks with natural language descriptions.
 ## Intended Use
+This model is designed to generate SVG code from text descriptions for educational and creative purposes.
 ## Limitations
 - Generated SVG may require validation
 - Performance depends on prompt clarity
 - Limited to SVG syntax and features seen during training
 ## Model Performance
+The model has been fine-tuned specifically for SVG generation tasks and should be used within this domain for best results.