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| import gradio as gr | |
| import spaces | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer | |
| import torch | |
| from threading import Thread | |
| phi4_model_path = "microsoft/Phi-4-reasoning-plus" | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| phi4_model = AutoModelForCausalLM.from_pretrained(phi4_model_path, device_map="auto", torch_dtype="auto") | |
| phi4_tokenizer = AutoTokenizer.from_pretrained(phi4_model_path) | |
| def generate_response(user_message, max_tokens, temperature, top_k, top_p, repetition_penalty, history_state): | |
| if not user_message.strip(): | |
| return history_state, history_state | |
| # Phi-4 model settings | |
| model = phi4_model | |
| tokenizer = phi4_tokenizer | |
| start_tag = "<|im_start|>" | |
| sep_tag = "<|im_sep|>" | |
| end_tag = "<|im_end|>" | |
| # Recommended prompt settings by Microsoft | |
| system_message = "Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:" | |
| prompt = f"{start_tag}system{sep_tag}{system_message}{end_tag}" | |
| for message in history_state: | |
| if message["role"] == "user": | |
| prompt += f"{start_tag}user{sep_tag}{message['content']}{end_tag}" | |
| elif message["role"] == "assistant" and message["content"]: | |
| prompt += f"{start_tag}assistant{sep_tag}{message['content']}{end_tag}" | |
| prompt += f"{start_tag}user{sep_tag}{user_message}{end_tag}{start_tag}assistant{sep_tag}" | |
| inputs = tokenizer(prompt, return_tensors="pt").to(device) | |
| do_sample = not (temperature == 1.0 and top_k >= 100 and top_p == 1.0) | |
| streamer = TextIteratorStreamer(tokenizer, skip_prompt=True) | |
| # sampling techniques | |
| generation_kwargs = { | |
| "input_ids": inputs["input_ids"], | |
| "attention_mask": inputs["attention_mask"], | |
| "max_new_tokens": int(max_tokens), | |
| "do_sample": True, | |
| "temperature": 0.8, | |
| "top_k": int(top_k), | |
| "top_p": 0.95, | |
| "repetition_penalty": repetition_penalty, | |
| "streamer": streamer, | |
| } | |
| thread = Thread(target=model.generate, kwargs=generation_kwargs) | |
| thread.start() | |
| # Stream the response | |
| assistant_response = "" | |
| new_history = history_state + [ | |
| {"role": "user", "content": user_message}, | |
| {"role": "assistant", "content": ""} | |
| ] | |
| for new_token in streamer: | |
| cleaned_token = new_token.replace("<|im_start|>", "").replace("<|im_sep|>", "").replace("<|im_end|>", "") | |
| assistant_response += cleaned_token | |
| new_history[-1]["content"] = assistant_response.strip() | |
| yield new_history, new_history | |
| yield new_history, new_history | |
| example_messages = { | |
| "Math problem": "Solve for x: 3x^2 + 6x - 9 = 0", | |
| "Algorithmic task": "Write a Python function to find the longest common subsequence of two strings.", | |
| "Reasoning puzzle": "There are 5 houses in a row, each with a different color. The person in each house has a different nationality, pet, drink, and cigarette brand. Given that: The Brit lives in the red house. The Swede keeps dogs. The Dane drinks tea. The green house is on the left of the white house. The green house owner drinks coffee. The person who smokes Pall Mall keeps birds. The owner of the yellow house smokes Dunhill. The man living in the center house drinks milk. The Norwegian lives in the first house. The man who smokes Blend lives next to the one who keeps cats. The man who keeps horses lives next to the man who smokes Dunhill. The owner who smokes Blue Master drinks beer. The German smokes Prince. The Norwegian lives next to the blue house. The man who smokes Blend has a neighbor who drinks water. Who owns the fish?" | |
| } | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.Markdown( | |
| """ | |
| # Phi-4-reasoning-plus Chatbot | |
| Welcome to the Phi-4-reasoning-plus Chatbot! This model is designed for advanced reasoning tasks and structured thinking. The model will provide responses with two sections: | |
| 1. **Thought section**: A detailed reasoning chain showing step-by-step analysis | |
| 2. **Solution section**: A concise, accurate final answer | |
| Adjust the settings on the left to customize the model's responses. For complex queries, consider increasing the max tokens. | |
| """ | |
| ) | |
| history_state = gr.State([]) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown("### Settings") | |
| max_tokens_slider = gr.Slider( | |
| minimum=64, | |
| maximum=32768, | |
| step=1024, | |
| value=4096, | |
| label="Max Tokens" | |
| ) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| temperature_slider = gr.Slider( | |
| minimum=0.1, | |
| maximum=2.0, | |
| value=0.8, | |
| label="Temperature" | |
| ) | |
| top_k_slider = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=50, | |
| label="Top-k" | |
| ) | |
| top_p_slider = gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| label="Top-p" | |
| ) | |
| repetition_penalty_slider = gr.Slider( | |
| minimum=1.0, | |
| maximum=2.0, | |
| value=1.0, | |
| label="Repetition Penalty" | |
| ) | |
| with gr.Column(scale=4): | |
| chatbot = gr.Chatbot(label="Chat", type="messages") | |
| with gr.Row(): | |
| user_input = gr.Textbox( | |
| label="Your message", | |
| placeholder="Type your message here...", | |
| scale=3 | |
| ) | |
| submit_button = gr.Button("Send", variant="primary", scale=1) | |
| clear_button = gr.Button("Clear", scale=1) | |
| gr.Markdown("**Try these examples:**") | |
| with gr.Row(): | |
| example1_button = gr.Button("Math problem") | |
| example2_button = gr.Button("Algorithmic task") | |
| example3_button = gr.Button("Reasoning puzzle") | |
| submit_button.click( | |
| fn=generate_response, | |
| inputs=[user_input, max_tokens_slider, temperature_slider, top_k_slider, top_p_slider, repetition_penalty_slider, history_state], | |
| outputs=[chatbot, history_state] | |
| ).then( | |
| fn=lambda: gr.update(value=""), | |
| inputs=None, | |
| outputs=user_input | |
| ) | |
| clear_button.click( | |
| fn=lambda: ([], []), | |
| inputs=None, | |
| outputs=[chatbot, history_state] | |
| ) | |
| example1_button.click( | |
| fn=lambda: gr.update(value=example_messages["Math problem"]), | |
| inputs=None, | |
| outputs=user_input | |
| ) | |
| example2_button.click( | |
| fn=lambda: gr.update(value=example_messages["Algorithmic task"]), | |
| inputs=None, | |
| outputs=user_input | |
| ) | |
| example3_button.click( | |
| fn=lambda: gr.update(value=example_messages["Reasoning puzzle"]), | |
| inputs=None, | |
| outputs=user_input | |
| ) | |
| demo.launch(ssr_mode=False) |