| import os |
| import gradio as gr |
|
|
| from openai import OpenAI |
|
|
| from optillm.cot_reflection import cot_reflection |
| from optillm.rto import round_trip_optimization |
| from optillm.z3_solver import Z3SymPySolverSystem |
| from optillm.self_consistency import advanced_self_consistency_approach |
| from optillm.plansearch import plansearch |
| from optillm.leap import leap |
| from optillm.reread import re2_approach |
|
|
|
|
| API_KEY = os.environ.get("OPENROUTER_API_KEY") |
|
|
| def compare_responses(message, model1, approach1, model2, approach2, system_message, max_tokens, temperature, top_p): |
| response1 = respond(message, [], model1, approach1, system_message, max_tokens, temperature, top_p) |
| response2 = respond(message, [], model2, approach2, system_message, max_tokens, temperature, top_p) |
| return response1, response2 |
|
|
| def parse_conversation(messages): |
| system_prompt = "" |
| conversation = [] |
| |
| for message in messages: |
| role = message['role'] |
| content = message['content'] |
| |
| if role == 'system': |
| system_prompt = content |
| elif role in ['user', 'assistant']: |
| conversation.append(f"{role.capitalize()}: {content}") |
| |
| initial_query = "\n".join(conversation) |
| return system_prompt, initial_query |
|
|
| def respond(message, history, model, approach, system_message, max_tokens, temperature, top_p): |
| try: |
| client = OpenAI(api_key=API_KEY, base_url="https://openrouter.ai/api/v1") |
| messages = [{"role": "system", "content": system_message}] |
| |
| for val in history: |
| if val[0]: |
| messages.append({"role": "user", "content": val[0]}) |
| if val[1]: |
| messages.append({"role": "assistant", "content": val[1]}) |
| |
| messages.append({"role": "user", "content": message}) |
| |
| if approach == "none": |
| response = client.chat.completions.create( |
| extra_headers={ |
| "HTTP-Referer": "https://github.com/codelion/optillm", |
| "X-Title": "optillm" |
| }, |
| model=model, |
| messages=messages, |
| max_tokens=max_tokens, |
| temperature=temperature, |
| top_p=top_p, |
| ) |
| return response.choices[0].message.content |
| else: |
| system_prompt, initial_query = parse_conversation(messages) |
| |
| if approach == 'rto': |
| final_response, _ = round_trip_optimization(system_prompt, initial_query, client, model) |
| elif approach == 'z3': |
| z3_solver = Z3SymPySolverSystem(system_prompt, client, model) |
| final_response, _ = z3_solver.process_query(initial_query) |
| elif approach == "self_consistency": |
| final_response, _ = advanced_self_consistency_approach(system_prompt, initial_query, client, model) |
| elif approach == "cot_reflection": |
| final_response, _ = cot_reflection(system_prompt, initial_query, client, model) |
| elif approach == 'plansearch': |
| response, _ = plansearch(system_prompt, initial_query, client, model) |
| final_response = response[0] |
| elif approach == 'leap': |
| final_response, _ = leap(system_prompt, initial_query, client, model) |
| elif approach == 're2': |
| final_response, _ = re2_approach(system_prompt, initial_query, client, model) |
| |
| return final_response |
| |
| except Exception as e: |
| error_message = f"Error in respond function: {str(e)}\nType: {type(e).__name__}" |
| print(error_message) |
|
|
| def create_model_dropdown(): |
| return gr.Dropdown( |
| [ "meta-llama/llama-3.1-8b-instruct:free", "nousresearch/hermes-3-llama-3.1-405b:free","meta-llama/llama-3.2-1b-instruct:free", |
| "mistralai/mistral-7b-instruct:free","mistralai/pixtral-12b:free","meta-llama/llama-3.1-70b-instruct:free", |
| "qwen/qwen-2-7b-instruct:free", "qwen/qwen-2-vl-7b-instruct:free", "google/gemma-2-9b-it:free", "liquid/lfm-40b:free", "meta-llama/llama-3.1-405b-instruct:free", |
| "openchat/openchat-7b:free", "meta-llama/llama-3.2-90b-vision-instruct:free", "meta-llama/llama-3.2-11b-vision-instruct:free", |
| "meta-llama/llama-3-8b-instruct:free", "meta-llama/llama-3.2-3b-instruct:free", "microsoft/phi-3-medium-128k-instruct:free", |
| "microsoft/phi-3-mini-128k-instruct:free", "huggingfaceh4/zephyr-7b-beta:free"], |
| value="meta-llama/llama-3.2-1b-instruct:free", label="Model" |
| ) |
|
|
| def create_approach_dropdown(): |
| return gr.Dropdown( |
| ["none", "leap", "plansearch", "cot_reflection", "rto", "self_consistency", "z3", "re2"], |
| value="none", label="Approach" |
| ) |
|
|
| html = """<iframe src="https://ghbtns.com/github-btn.html?user=codelion&repo=optillm&type=star&count=true&size=large" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> |
| """ |
|
|
| with gr.Blocks() as demo: |
| gr.Markdown("# OptiLLM - Optimizing LLM Inference") |
| gr.HTML(html) |
| |
| with gr.Row(): |
| system_message = gr.Textbox(value="", label="System message") |
| max_tokens = gr.Slider(minimum=1, maximum=4096, value=1024, step=1, label="Max new tokens") |
| temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature") |
| top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)") |
| |
| with gr.Tabs(): |
| with gr.TabItem("Chat"): |
| model = create_model_dropdown() |
| approach = create_approach_dropdown() |
| chatbot = gr.Chatbot() |
| msg = gr.Textbox() |
| with gr.Row(): |
| submit = gr.Button("Submit") |
| clear = gr.Button("Clear") |
|
|
| def user(user_message, history): |
| return "", history + [[user_message, None]] |
|
|
| def bot(history, model, approach, system_message, max_tokens, temperature, top_p): |
| user_message = history[-1][0] |
| bot_message = respond(user_message, history[:-1], model, approach, system_message, max_tokens, temperature, top_p) |
| history[-1][1] = bot_message |
| return history |
|
|
| msg.submit(user, [msg, chatbot], [msg, chatbot]).then( |
| bot, [chatbot, model, approach, system_message, max_tokens, temperature, top_p], chatbot |
| ) |
| submit.click(user, [msg, chatbot], [msg, chatbot]).then( |
| bot, [chatbot, model, approach, system_message, max_tokens, temperature, top_p], chatbot |
| ) |
| clear.click(lambda: None, None, chatbot, queue=False) |
|
|
| with gr.TabItem("Compare"): |
| with gr.Row(): |
| model1 = create_model_dropdown() |
| approach1 = create_approach_dropdown() |
| model2 = create_model_dropdown() |
| approach2 = create_approach_dropdown() |
| |
| compare_input = gr.Textbox(label="Enter your message for comparison") |
| compare_button = gr.Button("Compare") |
| |
| with gr.Row(): |
| output1 = gr.Textbox(label="Response 1") |
| output2 = gr.Textbox(label="Response 2") |
| |
| compare_button.click( |
| compare_responses, |
| inputs=[compare_input, model1, approach1, model2, approach2, system_message, max_tokens, temperature, top_p], |
| outputs=[output1, output2] |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
