Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the model and tokenizer
+model_name = "Lyte/Llama-3.2-3B-Overthinker"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+
+def generate_response_stream(prompt, max_tokens, temperature, top_p, repeat_penalty, num_steps=4):
+    messages = [{"role": "user", "content": prompt}]
+    
+    # Generate reasoning
+    reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
+    reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
+    
+    reasoning_ids = model.generate(
+        **reasoning_inputs, 
+        max_new_tokens=max_tokens // 3,
+        temperature=temperature,
+        top_p=top_p,
+        repetition_penalty=repeat_penalty
+    )
+    reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+    yield reasoning_output, "", ""
+    
+    # Generate thinking (step-by-step and verifications)
+    messages.append({"role": "reasoning", "content": reasoning_output})
+    thinking_template = tokenizer.apply_chat_template(messages, tokenize=False, add_thinking_prompt=True, num_steps=num_steps)
+    thinking_inputs = tokenizer(thinking_template, return_tensors="pt").to(model.device)
+    
+    thinking_ids = model.generate(
+        **thinking_inputs, 
+        max_new_tokens=max_tokens // 3,
+        temperature=temperature,
+        top_p=top_p,
+        repetition_penalty=repeat_penalty
+    )
+    thinking_output = tokenizer.decode(thinking_ids[0, thinking_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+    yield reasoning_output, thinking_output, ""
+    
+    # Generate final answer
+    messages.append({"role": "thinking", "content": thinking_output})
+    answer_template = tokenizer.apply_chat_template(messages, tokenize=False, add_answer_prompt=True)
+    answer_inputs = tokenizer(answer_template, return_tensors="pt").to(model.device)
+    
+    answer_ids = model.generate(
+        **answer_inputs, 
+        max_new_tokens=max_tokens // 3,
+        temperature=temperature,
+        top_p=top_p,
+        repetition_penalty=repeat_penalty
+    )
+    answer_output = tokenizer.decode(answer_ids[0, answer_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+    yield reasoning_output, thinking_output, answer_output
+
+with gr.Blocks() as iface:
+    gr.Markdown("# Llama-3.2-3B Overthinker Customizable Steps, Please Duplicate and run with GPU if you can! T4 is fine!")
+    gr.Markdown("Generate responses using the Llama-3.2-3B Reasoning model.")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            prompt = gr.Textbox(lines=5, label="Prompt")
+            generate_button = gr.Button("Generate Response")
+        with gr.Column(scale=1):
+            max_tokens = gr.Slider(minimum=512, maximum=32768, value=8192, label="Max Number of Tokens")
+            temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.8, label="Temperature")
+            top_p = gr.Slider(minimum=0.01, maximum=0.99, value=0.95, label="Top P")
+            repeat_penalty = gr.Slider(minimum=0.5, maximum=2, value=1.1, label="Repeat Penalty")
+            num_steps = gr.Slider(minimum=1, maximum=10, value=4, label="Max Number of Steps")
+    
+    reasoning_output = gr.Textbox(lines=5, label="Reasoning")
+    with gr.Accordion("Thinking Process", open=False):
+        thinking_output = gr.Textbox(lines=10, label="Step-by-Step Thinking")
+    answer_output = gr.Textbox(lines=5, label="Final Answer")
+    
+    generate_button.click(
+        fn=generate_response_stream,
+        inputs=[prompt, max_tokens, temperature, top_p, repeat_penalty, num_steps],
+        outputs=[reasoning_output, thinking_output, answer_output]
+    )
+
+iface.launch()