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app.py

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+
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+
+# --- Configuration ---
+BASE_MODEL_ID = "Qwen/Qwen3-0.6B"
+ADAPTER_MODEL_ID = "4rduino/Qwen3-0.6B-dieter-sft"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# --- Model Loading ---
+
+@gr.on(startup=True)
+def load_models():
+    """
+    Load models on application startup.
+    This function is decorated with @gr.on(startup=True) to run once when the app starts.
+    """
+    global base_model, finetuned_model, tokenizer
+
+    print("Loading base model and tokenizer...")
+    
+    # Use 4-bit quantization for memory efficiency
+    quantization_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.float16,
+    )
+
+    base_model = AutoModelForCausalLM.from_pretrained(
+        BASE_MODEL_ID,
+        quantization_config=quantization_config,
+        device_map="auto",
+        trust_remote_code=True,
+    )
+    
+    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID, trust_remote_code=True)
+    
+    print("Base model loaded.")
+
+    print("Loading and applying LoRA adapter...")
+    # Apply the adapter to the base model to get the fine-tuned model
+    finetuned_model = PeftModel.from_pretrained(base_model, ADAPTER_MODEL_ID)
+    
+    # Note: After merging, the model is no longer a PeftModel, but a normal CausalLM model.
+    # We will keep it as a PeftModel to avoid extra memory usage from creating a new merged model object.
+    
+    print("Models are ready!")
+
+
+def generate_text(prompt, temperature, max_new_tokens):
+    """
+    Generate text from both the base and the fine-tuned model.
+    """
+    if temperature <= 0:
+        temperature = 0.01
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(DEVICE)
+    
+    generate_kwargs = {
+        "max_new_tokens": int(max_new_tokens),
+        "temperature": float(temperature),
+        "do_sample": True,
+        "pad_token_id": tokenizer.eos_token_id,
+    }
+
+    # --- Generate from Base Model ---
+    print("Generating from base model...")
+    base_outputs = base_model.generate(**inputs, **generate_kwargs)
+    base_text = tokenizer.decode(base_outputs[0], skip_special_tokens=True)
+
+    # --- Generate from Fine-tuned Model ---
+    print("Generating from fine-tuned model...")
+    finetuned_outputs = finetuned_model.generate(**inputs, **generate_kwargs)
+    finetuned_text = tokenizer.decode(finetuned_outputs[0], skip_special_tokens=True)
+    
+    print("Generation complete.")
+    
+    # Return only the newly generated part of the text
+    base_response = base_text[len(prompt):]
+    finetuned_response = finetuned_text[len(prompt):]
+    
+    return base_response, finetuned_response
+
+# --- Gradio Interface ---
+
+css = """
+h1 { text-align: center; }
+.gr-box { border-radius: 10px !important; }
+.gr-button { background-color: #4CAF50 !important; color: white !important; }
+"""
+
+with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
+    gr.Markdown("# 🤖 Model Comparison: Base vs. Fine-tuned 'Dieter'")
+    gr.Markdown(
+        "Enter a prompt to see how the fine-tuned 'Dieter' model compares to the original Qwen-0.6B base model. "
+        "The 'Dieter' model was fine-tuned for a creative director persona."
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            prompt = gr.Textbox(
+                label="Your Prompt",
+                placeholder="e.g., Write a tagline for a new brand of sparkling water.",
+                lines=4,
+            )
+            with gr.Accordion("Generation Settings", open=False):
+                temperature = gr.Slider(
+                    minimum=0.0, maximum=2.0, value=0.7, step=0.1, label="Temperature"
+                )
+                max_new_tokens = gr.Slider(
+                    minimum=50, maximum=512, value=150, step=1, label="Max New Tokens"
+                )
+            btn = gr.Button("Generate", variant="primary")
+
+        with gr.Column(scale=3):
+            with gr.Tabs():
+                with gr.TabItem("Side-by-Side"):
+                    with gr.Row():
+                        out_base = gr.Textbox(label="Base Model Output", lines=12, interactive=False)
+                        out_finetuned = gr.Textbox(label="Fine-tuned 'Dieter' Output", lines=12, interactive=False)
+
+    btn.click(
+        fn=generate_text,
+        inputs=[prompt, temperature, max_new_tokens],
+        outputs=[out_base, out_finetuned],
+        api_name="compare"
+    )
+    
+    gr.Examples(
+        [
+            ["Write a creative brief for a new, eco-friendly sneaker brand."],
+            ["Generate three concepts for a new fragrance campaign targeting Gen Z."],
+            ["What's a bold, unexpected idea for a car commercial?"],
+            ["Give me some feedback on this headline: 'The Future of Coffee is Here.'"],
+        ],
+        inputs=[prompt],
+    )
+
+demo.launch()