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

5a8bbe6

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1 Parent(s): 3621384

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README.md CHANGED Viewed

@@ -4,15 +4,17 @@ 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:
 - example_title: "Simple Circle"
   text: "Create a red circle"
@@ -24,20 +26,28 @@ widget:
 # SVG Code Generator
-This is a fine-tuned LoRA adapter for generating SVG code from natural language descriptions.
 ## 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
-Load the model using the transformers library and PEFT for LoRA adapters. Use natural language prompts to generate SVG code.
 ## Training Data
@@ -55,4 +65,10 @@ This model is designed to generate SVG code from text descriptions for education
 ## Model Performance
-The model has been fine-tuned specifically for SVG generation tasks and should be used within this domain for best results.

 tags:
 - code-generation
 - svg
 - fine-tuned
+- fp16
+- vllm
+- merged
 language:
 - en
 pipeline_tag: text-generation
 library_name: transformers
+model_type: qwen
 inference: true
+torch_dtype: float16
 widget:
 - example_title: "Simple Circle"
   text: "Create a red circle"
 # SVG Code Generator
+This is a fine-tuned model for generating SVG code from natural language descriptions. The model has been merged with the base model weights and optimized in fp16 format.
 ## Model Details
 - **Model Name**: model_v10
 - **Base Model**: qwen3-0.6B
+- **Training Method**: Fine-tuning with merged weights
 - **Task**: Text-to-SVG code generation
+- **Model Type**: Merged Qwen model
+- **Precision**: fp16
+- **Library**: Transformers, vLLM compatible
+- **Format**: Merged model (not adapter-based)
 ## Usage
+### With Transformers
+Load the model directly using the transformers library:
+### With vLLM
+This model supports vLLM for high-performance inference in fp16 format.
 ## Training Data
 ## Model Performance
+The model has been fine-tuned specifically for SVG generation tasks with merged weights for optimal performance.
+## Technical Details
+- **Precision**: fp16 for memory efficiency
+- **Compatibility**: vLLM supported for high-throughput inference
+- **Architecture**: Merged fine-tuned weights (no adapters required)