Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+# 1. Setup Model IDs
+base_model_id = "unsloth/Qwen2.5-3B-Instruct"
+lora_model_id = "10Aizen01/qwen-2.5-3b-engine-simulator-beta" # Your repo!
+
+# 2. Load Tokenizer and Base Model
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id, 
+    torch_dtype=torch.float16, 
+    device_map="auto"
+)
+
+# 3. Load your LoRA adapters (The 131MB file)
+model = PeftModel.from_pretrained(base_model, lora_model_id)
+
+def generate_engine_code(prompt):
+    inputs = tokenizer(f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n", return_tensors="pt").to("cuda")
+    outputs = model.generate(**inputs, max_new_tokens=512)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# 4. Create the Web UI
+demo = gr.Interface(
+    fn=generate_engine_code,
+    inputs=gr.Textbox(label="Describe your engine (e.g., V8, 4.0L, 9000 RPM)"),
+    outputs=gr.Code(label="Generated .mr Script", language="cpp"),
+    title="Engine Simulator AI Assistant"
+)
+
+demo.launch()