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README.md
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@@ -44,6 +44,34 @@ This is a fine-tuned model for generating SVG code from natural language descrip
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### With Transformers
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Load the model directly using the transformers library:
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### With vLLM
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### With Transformers
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Load the model directly using the transformers library:
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```
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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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