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README.md

@@ -24,6 +24,38 @@ This is a fine-tuned LoRA adapter for generating SVG code from natural language
 ## Usage
 
 Load the model using the transformers library and PEFT for LoRA adapters. Use natural language prompts to generate SVG code.
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# Load base model and tokenizer
+base_model_name = "your-base-model-name"  # Replace with actual base model
+model = AutoModelForCausalLM.from_pretrained(base_model_name)
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(model, "your_username/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)
+```
 
 ## Training Data