| --- |
| 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) |
| |
