104 lines
2.6 KiB
Markdown
104 lines
2.6 KiB
Markdown
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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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