Create app.py

app.py

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+# LoRA Inference Gradio Space Demo
+
+import gradio as gr
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit",
+    device_map="auto",
+    torch_dtype="auto"
+)
+
+# Load the LoRA adapter
+model = PeftModel.from_pretrained(
+    base_model, 
+    "ZennyKenny/GPRO_LoRA_Qwen_3B"
+)
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit")
+
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=50)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+zk_qwen = gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
+    outputs=gr.Textbox(label="Response"),
+    title="LoRA Model Inference",
+    description="Demo your LoRA model with Hugging Face Gradio."
+)
+
+zk_qwen.launch()