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--- |
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license: apache-2.0 |
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base_model: qwen3-0.6B |
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tags: |
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- code-generation |
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- svg |
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- fine-tuned |
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- fp16 |
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- vllm |
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- merged |
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language: |
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- en |
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pipeline_tag: text-generation |
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library_name: transformers |
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model_type: qwen |
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inference: true |
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torch_dtype: float16 |
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widget: |
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- example_title: "Simple Circle" |
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text: "Create a red circle" |
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- example_title: "Rectangle with Border" |
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text: "Draw a blue rectangle with black border" |
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- example_title: "Complex Shape" |
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text: "Generate a star with 5 points in yellow" |
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--- |
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# SVG Code Generator |
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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. |
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## Model Details |
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- **Model Name**: model_v15 |
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- **Base Model**: qwen3-0.6B |
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- **Training Method**: Fine-tuning with merged weights |
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- **Task**: Text-to-SVG code generation |
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- **Model Type**: Merged Qwen model |
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- **Precision**: fp16 |
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- **Library**: Transformers, vLLM compatible |
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- **Format**: Merged model (not adapter-based) |
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## Usage |
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### With Transformers |
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Load the model directly using the transformers library: |
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```python |
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# Load base model and tokenizer |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("vinoku89/svg-code-generator") |
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model = AutoModelForCausalLM.from_pretrained("vinoku89/svg-code-generator") |
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# Generate SVG code |
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prompt = "Create a blue circle with radius 50" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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# Generate with parameters |
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outputs = model.generate( |
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**inputs, |
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max_length=200, |
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temperature=0.7, |
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do_sample=True, |
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pad_token_id=tokenizer.eos_token_id |
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) |
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# Decode the generated SVG code |
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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svg_code = generated_text[len(prompt):].strip() |
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print("Generated SVG:") |
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print(svg_code) |
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``` |
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### With vLLM |
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This model supports vLLM for high-performance inference in fp16 format. |
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## Training Data |
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The model was trained on SVG code generation tasks with natural language descriptions. |
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## Intended Use |
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This model is designed to generate SVG code from text descriptions for educational and creative purposes. |
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## Limitations |
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- Generated SVG may require validation |
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- Performance depends on prompt clarity |
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- Limited to SVG syntax and features seen during training |
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## Model Performance |
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The model has been fine-tuned specifically for SVG generation tasks with merged weights for optimal performance. |
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## Technical Details |
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- **Precision**: fp16 for memory efficiency |
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- **Compatibility**: vLLM supported for high-throughput inference |
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- **Architecture**: Merged fine-tuned weights (no adapters required) |
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