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)