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Model: vinoku89/svg-code-generator
Source: Original Platform

README.md

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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)