""" Chatbot Arena (battle) tab. Users chat with two anonymous models. """ import json import time import gradio as gr import numpy as np from fastchat.constants import ( MODERATION_MSG, CONVERSATION_LIMIT_MSG, SLOW_MODEL_MSG, INPUT_CHAR_LEN_LIMIT, CONVERSATION_TURN_LIMIT, ) from fastchat.model.model_adapter import get_conversation_template from fastchat.serve.gradio_block_arena_named import flash_buttons from fastchat.serve.gradio_web_server import ( State, bot_response, get_conv_log_filename, no_change_btn, enable_btn, disable_btn, invisible_btn, acknowledgment_md, get_ip, get_model_description_md, ) from fastchat.utils import ( build_logger, moderation_filter, ) logger = build_logger("gradio_web_server_multi", "gradio_web_server_multi.log") num_sides = 2 enable_moderation = False anony_names = ["", ""] models = [] def set_global_vars_anony(enable_moderation_): global enable_moderation enable_moderation = enable_moderation_ def load_demo_side_by_side_anony(models_, url_params): global models models = models_ states = (None,) * num_sides selector_updates = ( gr.Markdown(visible=True), gr.Markdown(visible=True), ) return states + selector_updates def vote_last_response(states, vote_type, model_selectors, request: gr.Request): with open(get_conv_log_filename(), "a") as fout: data = { "tstamp": round(time.time(), 4), "type": vote_type, "models": [x for x in model_selectors], "states": [x.dict() for x in states], "ip": get_ip(request), } fout.write(json.dumps(data) + "\n") if ":" not in model_selectors[0]: for i in range(5): names = ( "### Model A: " + states[0].model_name, "### Model B: " + states[1].model_name, ) yield names + ("",) + (disable_btn,) * 4 time.sleep(0.1) else: names = ( "### Model A: " + states[0].model_name, "### Model B: " + states[1].model_name, ) yield names + ("",) + (disable_btn,) * 4 def leftvote_last_response( state0, state1, model_selector0, model_selector1, request: gr.Request ): logger.info(f"leftvote (anony). ip: {get_ip(request)}") for x in vote_last_response( [state0, state1], "leftvote", [model_selector0, model_selector1], request ): yield x def rightvote_last_response( state0, state1, model_selector0, model_selector1, request: gr.Request ): logger.info(f"rightvote (anony). ip: {get_ip(request)}") for x in vote_last_response( [state0, state1], "rightvote", [model_selector0, model_selector1], request ): yield x def tievote_last_response( state0, state1, model_selector0, model_selector1, request: gr.Request ): logger.info(f"tievote (anony). ip: {get_ip(request)}") for x in vote_last_response( [state0, state1], "tievote", [model_selector0, model_selector1], request ): yield x def bothbad_vote_last_response( state0, state1, model_selector0, model_selector1, request: gr.Request ): logger.info(f"bothbad_vote (anony). ip: {get_ip(request)}") for x in vote_last_response( [state0, state1], "bothbad_vote", [model_selector0, model_selector1], request ): yield x def regenerate(state0, state1, request: gr.Request): logger.info(f"regenerate (anony). ip: {get_ip(request)}") states = [state0, state1] for i in range(num_sides): states[i].conv.update_last_message(None) return states + [x.to_gradio_chatbot() for x in states] + [""] + [disable_btn] * 6 def clear_history(request: gr.Request): logger.info(f"clear_history (anony). ip: {get_ip(request)}") return ( [None] * num_sides + [None] * num_sides + anony_names + [""] + [invisible_btn] * 4 + [disable_btn] * 2 + [""] ) def share_click(state0, state1, model_selector0, model_selector1, request: gr.Request): logger.info(f"share (anony). ip: {get_ip(request)}") if state0 is not None and state1 is not None: vote_last_response( [state0, state1], "share", [model_selector0, model_selector1], request ) SAMPLING_WEIGHTS = { # tier 0 "gpt-4": 4, "gpt-4-0314": 4, "gpt-4-0613": 4, "gpt-4-turbo": 4, "gpt-4-1106-preview": 4, "gpt-4-0125-preview": 4, "gpt-3.5-turbo-0613": 2, "gpt-3.5-turbo-1106": 2, "gpt-3.5-turbo-0125": 4, "claude-2.1": 4, "claude-2.0": 2, "claude-1": 2, "claude-instant-1": 2, "gemini-pro": 4, "gemini-pro-dev-api": 4, "bard-jan-24-gemini-pro": 4, "bard-feb-2024": 4, "mixtral-8x7b-instruct-v0.1": 4, "mistral-medium": 4, "qwen1.5-72b-chat": 4, "qwen1.5-7b-chat": 2, "qwen1.5-4b-chat": 2, "nous-hermes-2-mixtral-8x7b-dpo": 2, "deepseek-llm-67b-chat": 2, "stripedhyena-nous-7b": 2, "openchat-3.5-0106": 2, "mistral-7b-instruct-v0.2": 2, "solar-10.7b-instruct-v1.0": 2, "dolphin-2.2.1-mistral-7b": 2, "starling-lm-7b-alpha": 2, "tulu-2-dpo-70b": 2, "yi-34b-chat": 2, "zephyr-7b-beta": 2, # tier 1 "deluxe-chat-v1.2": 4, "llama-2-70b-chat": 4, "llama-2-13b-chat": 2, "llama-2-7b-chat": 2, "mistral-7b-instruct": 2, "codellama-34b-instruct": 1.5, "vicuna-33b": 2, "vicuna-13b": 1.5, "wizardlm-13b": 1.5, "qwen-14b-chat": 1.5, # tier 2 "pplx-7b-online": 1, "pplx-70b-online": 1, "openhermes-2.5-mistral-7b": 1.0, "llama2-70b-steerlm-chat": 1.0, "chatglm3-6b": 1.0, "openchat-3.5": 1.0, "wizardlm-70b": 1.0, "vicuna-7b": 1.0, "chatglm2-6b": 1.0, # deprecated "zephyr-7b-alpha": 1.5, "codellama-13b-instruct": 1.0, "mpt-30b-chat": 1.5, "guanaco-33b": 1.0, "fastchat-t5-3b": 0.5, "alpaca-13b": 0.5, "mpt-7b-chat": 0.1, "oasst-pythia-12b": 0.1, "RWKV-4-Raven-14B": 0.1, "gpt4all-13b-snoozy": 0.1, "koala-13b": 0.1, "stablelm-tuned-alpha-7b": 0.1, "dolly-v2-12b": 0.1, "llama-13b": 0.1, "chatglm-6b": 0.5, "deluxe-chat-v1": 4, "palm-2": 1.5, } # target model sampling weights will be boosted. BATTLE_TARGETS = { "gpt-4": {"gpt-4-0314", "claude-2.1", "gpt-4-1106-preview"}, "gpt-4-0613": {"gpt-4-0314", "claude-2.1", "gpt-4-1106-preview"}, "gpt-4-0314": { "gpt-4-1106-preview", "gpt-4-0613", "claude-2.1", "gpt-3.5-turbo-0613", }, "gpt-4-1106-preview": { "gpt-4-0613", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-1106", "claude-2.1", "bard-feb-2024", }, "gpt-4-0125-preview": { "gpt-4-1106-preview", "gpt-4-0613", "gpt-3.5-turbo-0613", "claude-2.1", "mistral-medium", "bard-feb-2024", }, "gpt-3.5-turbo-0613": {"claude-instant-1", "gpt-4-0613", "claude-2.1"}, "gpt-3.5-turbo-1106": {"gpt-4-0613", "claude-instant-1", "gpt-3.5-turbo-0613"}, "gpt-3.5-turbo-0125": { "gpt-4-0613", "gpt-4-1106-preview", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-1106", "mixtral-8x7b-instruct-v0.1", }, "qwen1.5-72b-chat": { "gpt-3.5-turbo-0125", "gpt-4-0613", "gpt-4-1106-preview", "llama-2-70b-chat", "mixtral-8x7b-instruct-v0.1", "mistral-medium", "yi-34b-chat", }, "qwen1.5-7b-chat": { "gpt-3.5-turbo-0125", "starling-lm-7b-alpha", "llama-2-70b-chat", "openchat-3.5", "mixtral-8x7b-instruct-v0.1", }, "qwen1.5-4b-chat": { "llama-2-70b-chat", "llama-2-13b-chat", "llama-2-7b-chat", "openchat-3.5", }, "openchat-3.5-0106": { "gpt-3.5-turbo-0125", "gpt-3.5-turbo-0613", "llama-2-70b-chat", "openchat-3.5", "mixtral-8x7b-instruct-v0.1", }, "nous-hermes-2-mixtral-8x7b-dpo": { "gpt-4-1106-preview", "claude-2.1", "mistral-medium", "gpt-3.5-turbo-0613", "mixtral-8x7b-instruct-v0.1", }, "mistral-7b-instruct-v0.2": { "llama-2-70b-chat", "mixtral-8x7b-instruct-v0.1", "starling-lm-7b-alpha", "openhermes-2.5-mistral-7b", }, "solar-10.7b-instruct-v1.0": { "mixtral-8x7b-instruct-v0.1", "gpt-3.5-turbo-0613", "llama-2-70b-chat", }, "mistral-medium": { "gpt-3.5-turbo-0125", "gpt-3.5-turbo-0613", "gpt-4-1106-preview", "mixtral-8x7b-instruct-v0.1", "bard-feb-2024", }, "mixtral-8x7b-instruct-v0.1": { "gpt-3.5-turbo-0125", "gpt-3.5-turbo-0613", "gpt-4-1106-preview", "llama-2-70b-chat", }, "claude-2.1": {"gpt-4-1106-preview", "gpt-4-0613", "claude-1"}, "claude-2.0": {"gpt-4-1106-preview", "gpt-4-0613", "claude-1"}, "claude-1": {"claude-2.1", "gpt-4-0613", "gpt-3.5-turbo-0613"}, "claude-instant-1": {"gpt-3.5-turbo-0125", "claude-2.1"}, "gemini-pro": {"gpt-4-1106-preview", "gpt-4-0613", "gpt-3.5-turbo-0613"}, "gemini-pro-dev-api": { "gpt-4-1106-preview", "gpt-4-0613", "gpt-3.5-turbo-0613", "bard-feb-2024", }, "bard-jan-24-gemini-pro": { "gpt-4-1106-preview", "gpt-4-0613", "gpt-3.5-turbo-0613", "gemini-pro-dev-api", }, "bard-feb-2024": { "gpt-4-1106-preview", "gpt-4-0613", "gpt-3.5-turbo-0613", "bard-jan-24-gemini-pro", }, "deepseek-llm-67b-chat": { "gpt-4-1106-preview", "gpt-4-turbo", "gpt-3.5-turbo-0613", }, "llama2-70b-steerlm-chat": { "llama-2-70b-chat", "tulu-2-dpo-70b", "yi-34b-chat", }, "stripedhyena-nous-7b": { "starling-lm-7b-alpha", "openhermes-2.5-mistral-7b", "mistral-7b-instruct", "llama-2-7b-chat", }, "deluxe-chat-v1.1": {"gpt-4-0613", "gpt-4-1106-preview"}, "deluxe-chat-v1.2": {"gpt-4-0613", "gpt-4-1106-preview"}, "pplx-7b-online": {"gpt-3.5-turbo-0125", "llama-2-70b-chat"}, "pplx-70b-online": {"gpt-3.5-turbo-0125", "llama-2-70b-chat"}, "openhermes-2.5-mistral-7b": { "gpt-3.5-turbo-0613", "openchat-3.5", "zephyr-7b-beta", }, "dolphin-2.2.1-mistral-7b": { "gpt-3.5-turbo-0613", "vicuna-33b", "starling-lm-7b-alpha", "openhermes-2.5-mistral-7b", }, "starling-lm-7b-alpha": {"gpt-3.5-turbo-0613", "openchat-3.5", "zephyr-7b-beta"}, "tulu-2-dpo-70b": {"gpt-3.5-turbo-0613", "vicuna-33b", "claude-instant-1"}, "yi-34b-chat": {"gpt-3.5-turbo-0613", "vicuna-33b", "claude-instant-1"}, "openchat-3.5": {"gpt-3.5-turbo-0613", "llama-2-70b-chat", "zephyr-7b-beta"}, "chatglm3-6b": {"yi-34b-chat", "qwen-14b-chat"}, "qwen-14b-chat": {"vicuna-13b", "llama-2-13b-chat", "llama-2-70b-chat"}, "zephyr-7b-alpha": {"mistral-7b-instruct", "llama-2-13b-chat"}, "zephyr-7b-beta": { "mistral-7b-instruct", "llama-2-13b-chat", "llama-2-7b-chat", "wizardlm-13b", }, "llama-2-70b-chat": {"gpt-3.5-turbo-0125", "claude-instant-1"}, "llama-2-13b-chat": {"mistral-7b-instruct", "vicuna-13b", "llama-2-70b-chat"}, "llama-2-7b-chat": {"mistral-7b-instruct", "vicuna-7b", "llama-2-13b-chat"}, "mistral-7b-instruct": { "llama-2-7b-chat", "llama-2-13b-chat", "llama-2-70b-chat", }, "vicuna-33b": {"llama-2-70b-chat", "gpt-3.5-turbo-0613", "claude-instant-1"}, "vicuna-13b": {"llama-2-13b-chat", "llama-2-70b-chat"}, "vicuna-7b": {"llama-2-7b-chat", "mistral-7b-instruct", "llama-2-13b-chat"}, "wizardlm-70b": {"gpt-3.5-turbo-0613", "vicuna-33b", "claude-instant-1"}, } SAMPLING_BOOST_MODELS = [ # "claude-2.1", # "gpt-4-0613", # "gpt-4-0314", # "gpt-4-1106-preview", # "gpt-4-0125-preview", "gpt-3.5-turbo-0125", # "mistral-medium", "nous-hermes-2-mixtral-8x7b-dpo", "openchat-3.5-0106", "qwen1.5-72b-chat", "qwen1.5-7b-chat", "qwen1.5-4b-chat", # "mistral-7b-instruct-v0.2", ] # outage models won't be sampled. OUTAGE_MODELS = [] def get_sample_weight(model): if model in OUTAGE_MODELS: return 0 weight = SAMPLING_WEIGHTS.get(model, 1.0) if model in SAMPLING_BOOST_MODELS: weight *= 5 return weight def get_battle_pair(): if len(models) == 1: return models[0], models[0] model_weights = [] for model in models: weight = get_sample_weight(model) model_weights.append(weight) total_weight = np.sum(model_weights) model_weights = model_weights / total_weight chosen_idx = np.random.choice(len(models), p=model_weights) chosen_model = models[chosen_idx] # for p, w in zip(models, model_weights): # print(p, w) rival_models = [] rival_weights = [] for model in models: if model == chosen_model: continue weight = get_sample_weight(model) if ( weight != 0 and chosen_model in BATTLE_TARGETS and model in BATTLE_TARGETS[chosen_model] ): # boost to 50% chance weight = total_weight / len(BATTLE_TARGETS[chosen_model]) rival_models.append(model) rival_weights.append(weight) # for p, w in zip(rival_models, rival_weights): # print(p, w) rival_weights = rival_weights / np.sum(rival_weights) rival_idx = np.random.choice(len(rival_models), p=rival_weights) rival_model = rival_models[rival_idx] swap = np.random.randint(2) if swap == 0: return chosen_model, rival_model else: return rival_model, chosen_model def add_text( state0, state1, model_selector0, model_selector1, text, request: gr.Request ): ip = get_ip(request) logger.info(f"add_text (anony). ip: {ip}. len: {len(text)}") states = [state0, state1] model_selectors = [model_selector0, model_selector1] # Init states if necessary if states[0] is None: assert states[1] is None model_left, model_right = get_battle_pair() states = [ State(model_left), State(model_right), ] if len(text) <= 0: for i in range(num_sides): states[i].skip_next = True return ( states + [x.to_gradio_chatbot() for x in states] + [""] + [ no_change_btn, ] * 6 + [""] ) model_list = [states[i].model_name for i in range(num_sides)] flagged = moderation_filter(text, model_list) if flagged: logger.info(f"violate moderation (anony). ip: {ip}. text: {text}") # overwrite the original text text = MODERATION_MSG conv = states[0].conv if (len(conv.messages) - conv.offset) // 2 >= CONVERSATION_TURN_LIMIT: logger.info(f"conversation turn limit. ip: {get_ip(request)}. text: {text}") for i in range(num_sides): states[i].skip_next = True return ( states + [x.to_gradio_chatbot() for x in states] + [CONVERSATION_LIMIT_MSG] + [ no_change_btn, ] * 6 + [""] ) text = text[:INPUT_CHAR_LEN_LIMIT] # Hard cut-off for i in range(num_sides): states[i].conv.append_message(states[i].conv.roles[0], text) states[i].conv.append_message(states[i].conv.roles[1], None) states[i].skip_next = False hint_msg = "" for i in range(num_sides): if "deluxe" in states[i].model_name: hint_msg = SLOW_MODEL_MSG return ( states + [x.to_gradio_chatbot() for x in states] + [""] + [ disable_btn, ] * 6 + [hint_msg] ) def bot_response_multi( state0, state1, temperature, top_p, max_new_tokens, request: gr.Request, ): logger.info(f"bot_response_multi (anony). ip: {get_ip(request)}") if state0 is None or state0.skip_next: # This generate call is skipped due to invalid inputs yield ( state0, state1, state0.to_gradio_chatbot(), state1.to_gradio_chatbot(), ) + (no_change_btn,) * 6 return states = [state0, state1] gen = [] for i in range(num_sides): gen.append( bot_response( states[i], temperature, top_p, max_new_tokens, request, apply_rate_limit=False, ) ) is_gemini = [] for i in range(num_sides): is_gemini.append(states[i].model_name in ["gemini-pro", "gemini-pro-dev-api"]) chatbots = [None] * num_sides iters = 0 while True: stop = True iters += 1 for i in range(num_sides): try: # yield gemini fewer times as its chunk size is larger # otherwise, gemini will stream too fast if not is_gemini[i] or (iters % 30 == 1 or iters < 3): ret = next(gen[i]) states[i], chatbots[i] = ret[0], ret[1] stop = False except StopIteration: pass yield states + chatbots + [disable_btn] * 6 if stop: break def build_side_by_side_ui_anony(models): notice_markdown = """ # ⚔️ Chatbot Arena: Benchmarking LLMs in the Wild | [Blog](https://lmsys.org/blog/2023-05-03-arena/) | [GitHub](https://github.com/lm-sys/FastChat) | [Paper](https://arxiv.org/abs/2306.05685) | [Dataset](https://github.com/lm-sys/FastChat/blob/main/docs/dataset_release.md) | [Twitter](https://twitter.com/lmsysorg) | [Discord](https://discord.gg/HSWAKCrnFx) | ## 📜 Rules - Ask any question to two anonymous models (e.g., ChatGPT, Claude, Llama) and vote for the better one! - You can continue chatting until you identify a winner. - Vote won't be counted if model identity is revealed during conversation. ## 🏆 Arena Elo [Leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) We collect **200K+** human votes to compute an Elo-based LLM leaderboard. Find out who is the 🥇LLM Champion! ## 👇 Chat now! """ states = [gr.State() for _ in range(num_sides)] model_selectors = [None] * num_sides chatbots = [None] * num_sides gr.Markdown(notice_markdown, elem_id="notice_markdown") with gr.Group(elem_id="share-region-anony"): with gr.Accordion( f"🔍 Expand to see the descriptions of {len(models)} models", open=False ): model_description_md = get_model_description_md(models) gr.Markdown(model_description_md, elem_id="model_description_markdown") with gr.Row(): for i in range(num_sides): label = "Model A" if i == 0 else "Model B" with gr.Column(): chatbots[i] = gr.Chatbot( label=label, elem_id="chatbot", height=550, show_copy_button=True, ) with gr.Row(): for i in range(num_sides): with gr.Column(): model_selectors[i] = gr.Markdown( anony_names[i], elem_id="model_selector_md" ) with gr.Row(): slow_warning = gr.Markdown("", elem_id="notice_markdown") with gr.Row(): leftvote_btn = gr.Button( value="👈 A is better", visible=False, interactive=False ) rightvote_btn = gr.Button( value="👉 B is better", visible=False, interactive=False ) tie_btn = gr.Button(value="🤝 Tie", visible=False, interactive=False) bothbad_btn = gr.Button( value="👎 Both are bad", visible=False, interactive=False ) with gr.Row(): textbox = gr.Textbox( show_label=False, placeholder="👉 Enter your prompt and press ENTER", elem_id="input_box", ) send_btn = gr.Button(value="Send", variant="primary", scale=0) with gr.Row() as button_row: clear_btn = gr.Button(value="🎲 New Round", interactive=False) regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=False) share_btn = gr.Button(value="📷 Share") with gr.Accordion("Parameters", open=False) as parameter_row: temperature = gr.Slider( minimum=0.0, maximum=1.0, value=0.7, step=0.1, interactive=True, label="Temperature", ) top_p = gr.Slider( minimum=0.0, maximum=1.0, value=1.0, step=0.1, interactive=True, label="Top P", ) max_output_tokens = gr.Slider( minimum=16, maximum=2048, value=1024, step=64, interactive=True, label="Max output tokens", ) gr.Markdown(acknowledgment_md, elem_id="ack_markdown") # Register listeners btn_list = [ leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, regenerate_btn, clear_btn, ] leftvote_btn.click( leftvote_last_response, states + model_selectors, model_selectors + [textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn], ) rightvote_btn.click( rightvote_last_response, states + model_selectors, model_selectors + [textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn], ) tie_btn.click( tievote_last_response, states + model_selectors, model_selectors + [textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn], ) bothbad_btn.click( bothbad_vote_last_response, states + model_selectors, model_selectors + [textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn], ) regenerate_btn.click( regenerate, states, states + chatbots + [textbox] + btn_list ).then( bot_response_multi, states + [temperature, top_p, max_output_tokens], states + chatbots + btn_list, ).then( flash_buttons, [], btn_list ) clear_btn.click( clear_history, None, states + chatbots + model_selectors + [textbox] + btn_list + [slow_warning], ) share_js = """ function (a, b, c, d) { const captureElement = document.querySelector('#share-region-anony'); html2canvas(captureElement) .then(canvas => { canvas.style.display = 'none' document.body.appendChild(canvas) return canvas }) .then(canvas => { const image = canvas.toDataURL('image/png') const a = document.createElement('a') a.setAttribute('download', 'chatbot-arena.png') a.setAttribute('href', image) a.click() canvas.remove() }); return [a, b, c, d]; } """ share_btn.click(share_click, states + model_selectors, [], js=share_js) textbox.submit( add_text, states + model_selectors + [textbox], states + chatbots + [textbox] + btn_list + [slow_warning], ).then( bot_response_multi, states + [temperature, top_p, max_output_tokens], states + chatbots + btn_list, ).then( flash_buttons, [], btn_list, ) send_btn.click( add_text, states + model_selectors + [textbox], states + chatbots + [textbox] + btn_list, ).then( bot_response_multi, states + [temperature, top_p, max_output_tokens], states + chatbots + btn_list, ).then( flash_buttons, [], btn_list ) return states + model_selectors