---
license: apache-2.0
base_model: qwen3-0.6B
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"
- example_title: "Rectangle with Border"
  text: "Draw a blue rectangle with black border"
- example_title: "Complex Shape"
  text: "Generate a star with 5 points in yellow"
---

# 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:

```python
# Load base model and tokenizer
from transformers import AutoTokenizer, AutoModelForCausalLM

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


# Generate SVG code
prompt = "Create a blue circle with radius 50"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate with parameters
outputs = model.generate(
    **inputs, 
    max_length=200,
    temperature=0.7,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

# Decode the generated SVG code
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
svg_code = generated_text[len(prompt):].strip()

print("Generated SVG:")
print(svg_code)
```

### With vLLM

This model supports vLLM for high-performance inference in fp16 format.

## 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 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)