Create app.py

app.py

@@ -0,0 +1,221 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from threading import Thread
+import spaces
+
+# Load model and tokenizer
+model_id = "openfree/Darwin-Qwen3-4B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+@spaces.GPU
+def generate_response(
+    message,
+    history,
+    temperature=0.7,
+    max_new_tokens=512,
+    top_p=0.9,
+    repetition_penalty=1.1,
+):
+    # Format conversation history
+    conversation = []
+    for user, assistant in history:
+        conversation.extend([
+            {"role": "user", "content": user},
+            {"role": "assistant", "content": assistant}
+        ])
+    conversation.append({"role": "user", "content": message})
+    
+    # Apply chat template if available
+    if hasattr(tokenizer, "apply_chat_template"):
+        text = tokenizer.apply_chat_template(
+            conversation,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+    else:
+        # Fallback formatting
+        text = "\n".join([f"User: {message}" if i["role"] == "user" 
+                         else f"Assistant: {message}" 
+                         for i in conversation])
+        text += "\nAssistant: "
+    
+    # Tokenize input
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=2048)
+    inputs = inputs.to(model.device)
+    
+    # Set up streaming
+    streamer = TextIteratorStreamer(
+        tokenizer, 
+        timeout=10.0, 
+        skip_prompt=True, 
+        skip_special_tokens=True
+    )
+    
+    # Generation parameters
+    gen_kwargs = dict(
+        inputs,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+    
+    # Start generation in separate thread
+    thread = Thread(target=model.generate, kwargs=gen_kwargs)
+    thread.start()
+    
+    # Stream output
+    response = ""
+    for new_text in streamer:
+        response += new_text
+        yield response
+    
+    thread.join()
+
+# Create Gradio interface
+with gr.Blocks(title="Darwin-Qwen3-4B Chat") as demo:
+    gr.Markdown(
+        """
+        # 🌱 Darwin-Qwen3-4B Interactive Chat
+        
+        Test the evolutionary merged model that combines the strengths of instruction-following and reasoning capabilities.
+        
+        **Model**: [openfree/Darwin-Qwen3-4B](https://huggingface.co/openfree/Darwin-Qwen3-4B)
+        
+        This model was created using the Darwin A2AP Enhanced v3.2 evolutionary algorithm, merging:
+        - Parent 1: Qwen/Qwen3-4B-Instruct-2507
+        - Parent 2: Qwen/Qwen3-4B-Thinking-2507
+        """
+    )
+    
+    chatbot = gr.Chatbot(
+        label="Chat History",
+        bubble_full_width=False,
+        height=400
+    )
+    
+    with gr.Row():
+        msg = gr.Textbox(
+            label="Your Message",
+            placeholder="Type your message here and press Enter...",
+            lines=2,
+            scale=4
+        )
+        submit_btn = gr.Button("Send", scale=1, variant="primary")
+    
+    with gr.Accordion("Advanced Settings", open=False):
+        temperature = gr.Slider(
+            minimum=0.1,
+            maximum=1.5,
+            value=0.7,
+            step=0.1,
+            label="Temperature (higher = more creative)"
+        )
+        max_new_tokens = gr.Slider(
+            minimum=64,
+            maximum=2048,
+            value=512,
+            step=64,
+            label="Max New Tokens"
+        )
+        top_p = gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.9,
+            step=0.05,
+            label="Top-p (nucleus sampling)"
+        )
+        repetition_penalty = gr.Slider(
+            minimum=1.0,
+            maximum=1.5,
+            value=1.1,
+            step=0.05,
+            label="Repetition Penalty"
+        )
+    
+    with gr.Row():
+        clear_btn = gr.Button("Clear Chat", variant="secondary")
+        
+    gr.Examples(
+        examples=[
+            "Explain quantum computing in simple terms.",
+            "Write a Python function to find prime numbers.",
+            "What are the key differences between machine learning and deep learning?",
+            "Suggest a healthy meal plan for a week.",
+            "How does photosynthesis work?",
+        ],
+        inputs=msg,
+        label="Example Prompts"
+    )
+    
+    # Event handlers
+    def user_submit(message, history):
+        return "", history + [[message, None]]
+    
+    def bot_respond(history, temperature, max_new_tokens, top_p, repetition_penalty):
+        message = history[-1][0]
+        history[-1][1] = ""
+        
+        for response in generate_response(
+            message, 
+            history[:-1],
+            temperature,
+            max_new_tokens,
+            top_p,
+            repetition_penalty
+        ):
+            history[-1][1] = response
+            yield history
+    
+    msg.submit(
+        user_submit, 
+        [msg, chatbot], 
+        [msg, chatbot]
+    ).then(
+        bot_respond,
+        [chatbot, temperature, max_new_tokens, top_p, repetition_penalty],
+        chatbot
+    )
+    
+    submit_btn.click(
+        user_submit, 
+        [msg, chatbot], 
+        [msg, chatbot]
+    ).then(
+        bot_respond,
+        [chatbot, temperature, max_new_tokens, top_p, repetition_penalty],
+        chatbot
+    )
+    
+    clear_btn.click(lambda: None, None, chatbot, queue=False)
+    
+    gr.Markdown(
+        """
+        ---
+        ### About Darwin Project
+        
+        The Darwin Project demonstrates a new paradigm in AI model creation through evolutionary algorithms.
+        This model showcases the fusion of different model capabilities at 1/10,000 the cost of traditional training.
+        
+        **Key Features:**
+        - Automated model merging without manual hyperparameter tuning
+        - Multi-objective optimization (accuracy, robustness, generalization)
+        - 5,000+ generation evolution process
+        
+        [GitHub](https://github.com/yourusername/darwin-project) | [Paper](https://arxiv.org/abs/xxxx.xxxxx) (Coming Soon)
+        """
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch(share=True)