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amkyawdev
Available on FriendliAI
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Model Details
Model Provider
amkyawdev
Model Tree
Input Modalities
Output Modalities
Supported Functionality
Load the model directly using the transformers library:
python
# Load base model and tokenizerfrom transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("vinoku89/svg-code-generator")model = AutoModelForCausalLM.from_pretrained("vinoku89/svg-code-generator") # Generate SVG codeprompt = "Create a blue circle with radius 50"inputs = tokenizer(prompt, return_tensors="pt") # Generate with parametersoutputs = model.generate( **inputs, max_length=200, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id) # Decode the generated SVG codegenerated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)svg_code = generated_text[len(prompt):].strip() print("Generated SVG:")print(svg_code)This model supports vLLM for high-performance inference in fp16 format.
The model was trained on SVG code generation tasks with natural language descriptions.
This model is designed to generate SVG code from text descriptions for educational and creative purposes.
The model has been fine-tuned specifically for SVG generation tasks with merged weights for optimal performance.
# Load base model and tokenizerfrom transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("vinoku89/svg-code-generator")model = AutoModelForCausalLM.from_pretrained("vinoku89/svg-code-generator") # Generate SVG codeprompt = "Create a blue circle with radius 50"inputs = tokenizer(prompt, return_tensors="pt") # Generate with parametersoutputs = model.generate( **inputs, max_length=200, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id) # Decode the generated SVG codegenerated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)svg_code = generated_text[len(prompt):].strip() print("Generated SVG:")print(svg_code)