Update app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# Load model and tokenizer
+def load_model(model_id):
+    # First load the base model
+    base_model_id = "microsoft/phi-2"
+    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+    
+    # Ensure tokenizer has a padding token
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    
+    base_model = AutoModelForCausalLM.from_pretrained(
+        base_model_id,
+        torch_dtype=torch.float16,
+        device_map="auto",
+        trust_remote_code=True
+    )
+    
+    # Load and merge the LoRA adapter
+    model = PeftModel.from_pretrained(base_model, model_id)
+    return model, tokenizer
+
+def generate_response(instruction, model, tokenizer, max_length=200, temperature=0.7, top_p=0.9):
+    # Format the input text
+    input_text = instruction.strip()
+    
+    # Tokenize input
+    inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+    
+    # Generate response
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_length,
+            temperature=temperature,
+            top_p=top_p,
+            num_return_sequences=1,
+            pad_token_id=tokenizer.eos_token_id,
+            do_sample=True
+        )
+    
+    # Decode and return the response
+    full_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Extract only the response part (what comes after the instruction)
+    if len(input_text) < len(full_text):
+        response = full_text[len(input_text):].strip()
+        return response
+    return full_text.strip()
+
+def create_demo(model_id):
+    # Load model and tokenizer
+    model, tokenizer = load_model(model_id)
+    
+    # Define the interface
+    def process_input(instruction, max_length, temperature, top_p):
+        try:
+            return generate_response(
+                instruction,
+                model,
+                tokenizer,
+                max_length=max_length,
+                temperature=temperature,
+                top_p=top_p
+            )
+        except Exception as e:
+            return f"Error generating response: {str(e)}"
+    
+    # Create the interface
+    demo = gr.Interface(
+        fn=process_input,
+        inputs=[
+            gr.Textbox(
+                label="Input Text",
+                placeholder="Enter your text here...",
+                lines=4
+            ),
+            gr.Slider(
+                minimum=50,
+                maximum=500,
+                value=150,
+                step=10,
+                label="Maximum Length"
+            ),
+            gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.7,
+                step=0.1,
+                label="Temperature"
+            ),
+            gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.9,
+                step=0.1,
+                label="Top P"
+            )
+        ],
+        outputs=gr.Textbox(label="Completion", lines=8),
+        title="Phi-2 GRPO Model Demo",
+        description="""This is a generative model trained using GRPO (Generative Reinforcement from Preference Optimization) 
+        on the TLDR dataset. The model was trained to generate completions of around 150 characters.
+        
+        You can adjust the generation parameters:
+        - **Maximum Length**: Controls the maximum length of the generated response
+        - **Temperature**: Higher values make the output more random, lower values make it more focused
+        - **Top P**: Controls the cumulative probability threshold for token sampling
+        """,
+        examples=[
+            ["The quick brown fox jumps over the lazy dog."],
+            ["In this tutorial, we will explore how to build a neural network for image classification."],
+            ["The best way to prepare for an interview is to"],
+            ["Python is a popular programming language because"]
+        ]
+    )
+    return demo
+
+if __name__ == "__main__":
+    # Use your model ID
+    model_id = "jatingocodeo/phi2-grpo"
+    demo = create_demo(model_id)
+    demo.launch()