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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_v15
- **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)
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