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| """A gradio app that renders a static leaderboard. This is used for Hugging Face Space.""" | |
| import ast | |
| import argparse | |
| import glob | |
| import pickle | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| # notebook_url = "https://colab.research.google.com/drive/1RAWb22-PFNI-X1gPVzc927SGUdfr6nsR?usp=sharing" | |
| notebook_url = "https://colab.research.google.com/drive/1KdwokPjirkTmpO_P1WByFNFiqxWQquwH#scrollTo=o_CpbkGEbhrK" | |
| basic_component_values = [None] * 6 | |
| leader_component_values = [None] * 5 | |
| def make_default_md(arena_df, elo_results): | |
| total_votes = sum(arena_df["num_battles"]) // 2 | |
| total_models = len(arena_df) | |
| leaderboard_md = f""" | |
| # 🏆 LMSYS Chatbot Arena Leaderboard | |
| | [Vote](https://chat.lmsys.org) | [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) | | |
| LMSYS [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) is a crowdsourced open platform for LLM evals. | |
| We've collected over **500,000** human preference votes to rank LLMs with the Elo ranking system. | |
| """ | |
| return leaderboard_md | |
| def make_arena_leaderboard_md(arena_df): | |
| total_votes = sum(arena_df["num_battles"]) // 2 | |
| total_models = len(arena_df) | |
| leaderboard_md = f""" | |
| Total #models: **{total_models}**. Total #votes: **{total_votes}**. Last updated: April 9, 2024. | |
| Contribute your vote 🗳️ at [chat.lmsys.org](https://chat.lmsys.org)! Find more analysis in the [notebook]({notebook_url}). | |
| """ | |
| return leaderboard_md | |
| def make_full_leaderboard_md(elo_results): | |
| leaderboard_md = f""" | |
| Three benchmarks are displayed: **Arena Elo**, **MT-Bench** and **MMLU**. | |
| - [Chatbot Arena](https://chat.lmsys.org/?arena) - a crowdsourced, randomized battle platform. We use 500K+ user votes to compute Elo ratings. | |
| - [MT-Bench](https://arxiv.org/abs/2306.05685): a set of challenging multi-turn questions. We use GPT-4 to grade the model responses. | |
| - [MMLU](https://arxiv.org/abs/2009.03300) (5-shot): a test to measure a model's multitask accuracy on 57 tasks. | |
| 💻 Code: The MT-bench scores (single-answer grading on a scale of 10) are computed by [fastchat.llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge). | |
| The MMLU scores are mostly computed by [InstructEval](https://github.com/declare-lab/instruct-eval). | |
| Higher values are better for all benchmarks. Empty cells mean not available. | |
| """ | |
| return leaderboard_md | |
| def make_leaderboard_md_live(elo_results): | |
| leaderboard_md = f""" | |
| # Leaderboard | |
| Last updated: {elo_results["last_updated_datetime"]} | |
| {elo_results["leaderboard_table"]} | |
| """ | |
| return leaderboard_md | |
| def update_elo_components(max_num_files, elo_results_file): | |
| log_files = get_log_files(max_num_files) | |
| # Leaderboard | |
| if elo_results_file is None: # Do live update | |
| battles = clean_battle_data(log_files) | |
| elo_results = report_elo_analysis_results(battles) | |
| leader_component_values[0] = make_leaderboard_md_live(elo_results) | |
| leader_component_values[1] = elo_results["win_fraction_heatmap"] | |
| leader_component_values[2] = elo_results["battle_count_heatmap"] | |
| leader_component_values[3] = elo_results["bootstrap_elo_rating"] | |
| leader_component_values[4] = elo_results["average_win_rate_bar"] | |
| # Basic stats | |
| basic_stats = report_basic_stats(log_files) | |
| md0 = f"Last updated: {basic_stats['last_updated_datetime']}" | |
| md1 = "### Action Histogram\n" | |
| md1 += basic_stats["action_hist_md"] + "\n" | |
| md2 = "### Anony. Vote Histogram\n" | |
| md2 += basic_stats["anony_vote_hist_md"] + "\n" | |
| md3 = "### Model Call Histogram\n" | |
| md3 += basic_stats["model_hist_md"] + "\n" | |
| md4 = "### Model Call (Last 24 Hours)\n" | |
| md4 += basic_stats["num_chats_last_24_hours"] + "\n" | |
| basic_component_values[0] = md0 | |
| basic_component_values[1] = basic_stats["chat_dates_bar"] | |
| basic_component_values[2] = md1 | |
| basic_component_values[3] = md2 | |
| basic_component_values[4] = md3 | |
| basic_component_values[5] = md4 | |
| def update_worker(max_num_files, interval, elo_results_file): | |
| while True: | |
| tic = time.time() | |
| update_elo_components(max_num_files, elo_results_file) | |
| durtaion = time.time() - tic | |
| print(f"update duration: {durtaion:.2f} s") | |
| time.sleep(max(interval - durtaion, 0)) | |
| def load_demo(url_params, request: gr.Request): | |
| logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}") | |
| return basic_component_values + leader_component_values | |
| def model_hyperlink(model_name, link): | |
| return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>' | |
| def load_leaderboard_table_csv(filename, add_hyperlink=True): | |
| lines = open(filename).readlines() | |
| heads = [v.strip() for v in lines[0].split(",")] | |
| rows = [] | |
| for i in range(1, len(lines)): | |
| row = [v.strip() for v in lines[i].split(",")] | |
| for j in range(len(heads)): | |
| item = {} | |
| for h, v in zip(heads, row): | |
| if h == "Arena Elo rating": | |
| if v != "-": | |
| v = int(ast.literal_eval(v)) | |
| else: | |
| v = np.nan | |
| elif h == "MMLU": | |
| if v != "-": | |
| v = round(ast.literal_eval(v) * 100, 1) | |
| else: | |
| v = np.nan | |
| elif h == "MT-bench (win rate %)": | |
| if v != "-": | |
| v = round(ast.literal_eval(v[:-1]), 1) | |
| else: | |
| v = np.nan | |
| elif h == "MT-bench (score)": | |
| if v != "-": | |
| v = round(ast.literal_eval(v), 2) | |
| else: | |
| v = np.nan | |
| item[h] = v | |
| if add_hyperlink: | |
| item["Model"] = model_hyperlink(item["Model"], item["Link"]) | |
| rows.append(item) | |
| return rows | |
| def build_basic_stats_tab(): | |
| empty = "Loading ..." | |
| basic_component_values[:] = [empty, None, empty, empty, empty, empty] | |
| md0 = gr.Markdown(empty) | |
| gr.Markdown("#### Figure 1: Number of model calls and votes") | |
| plot_1 = gr.Plot(show_label=False) | |
| with gr.Row(): | |
| with gr.Column(): | |
| md1 = gr.Markdown(empty) | |
| with gr.Column(): | |
| md2 = gr.Markdown(empty) | |
| with gr.Row(): | |
| with gr.Column(): | |
| md3 = gr.Markdown(empty) | |
| with gr.Column(): | |
| md4 = gr.Markdown(empty) | |
| return [md0, plot_1, md1, md2, md3, md4] | |
| def get_full_table(arena_df, model_table_df): | |
| values = [] | |
| for i in range(len(model_table_df)): | |
| row = [] | |
| model_key = model_table_df.iloc[i]["key"] | |
| model_name = model_table_df.iloc[i]["Model"] | |
| # model display name | |
| row.append(model_name) | |
| if model_key in arena_df.index: | |
| idx = arena_df.index.get_loc(model_key) | |
| row.append(round(arena_df.iloc[idx]["rating"])) | |
| else: | |
| row.append(np.nan) | |
| row.append(model_table_df.iloc[i]["MT-bench (score)"]) | |
| row.append(model_table_df.iloc[i]["MMLU"]) | |
| # Organization | |
| row.append(model_table_df.iloc[i]["Organization"]) | |
| # license | |
| row.append(model_table_df.iloc[i]["License"]) | |
| values.append(row) | |
| values.sort(key=lambda x: -x[1] if not np.isnan(x[1]) else 1e9) | |
| return values | |
| def get_arena_table(arena_df, model_table_df): | |
| # sort by rating | |
| arena_df = arena_df.sort_values(by=["final_ranking", "rating"], ascending=[True, False]) | |
| values = [] | |
| for i in range(len(arena_df)): | |
| row = [] | |
| model_key = arena_df.index[i] | |
| model_name = model_table_df[model_table_df["key"] == model_key]["Model"].values[ | |
| 0 | |
| ] | |
| # rank | |
| ranking = arena_df.iloc[i].get("final_ranking") or i+1 | |
| row.append(ranking) | |
| # model display name | |
| row.append(model_name) | |
| # elo rating | |
| row.append(round(arena_df.iloc[i]["rating"])) | |
| upper_diff = round( | |
| arena_df.iloc[i]["rating_q975"] - arena_df.iloc[i]["rating"] | |
| ) | |
| lower_diff = round( | |
| arena_df.iloc[i]["rating"] - arena_df.iloc[i]["rating_q025"] | |
| ) | |
| row.append(f"+{upper_diff}/-{lower_diff}") | |
| # num battles | |
| row.append(round(arena_df.iloc[i]["num_battles"])) | |
| # Organization | |
| row.append( | |
| model_table_df[model_table_df["key"] == model_key]["Organization"].values[0] | |
| ) | |
| # license | |
| row.append( | |
| model_table_df[model_table_df["key"] == model_key]["License"].values[0] | |
| ) | |
| cutoff_date = model_table_df[model_table_df["key"] == model_key]["Knowledge cutoff date"].values[0] | |
| if cutoff_date == "-": | |
| row.append("Unknown") | |
| else: | |
| row.append(cutoff_date) | |
| values.append(row) | |
| return values | |
| def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=False): | |
| if elo_results_file is None: # Do live update | |
| default_md = "Loading ..." | |
| p1 = p2 = p3 = p4 = None | |
| else: | |
| with open(elo_results_file, "rb") as fin: | |
| elo_results = pickle.load(fin) | |
| if "full" in elo_results: | |
| elo_results = elo_results["full"] | |
| p1 = elo_results["win_fraction_heatmap"] | |
| p2 = elo_results["battle_count_heatmap"] | |
| p3 = elo_results["bootstrap_elo_rating"] | |
| p4 = elo_results["average_win_rate_bar"] | |
| arena_df = elo_results["leaderboard_table_df"] | |
| default_md = make_default_md(arena_df, elo_results) | |
| md_1 = gr.Markdown(default_md, elem_id="leaderboard_markdown") | |
| if leaderboard_table_file: | |
| data = load_leaderboard_table_csv(leaderboard_table_file) | |
| model_table_df = pd.DataFrame(data) | |
| with gr.Tabs() as tabs: | |
| # arena table | |
| arena_table_vals = get_arena_table(arena_df, model_table_df) | |
| with gr.Tab("Arena Elo", id=0): | |
| md = make_arena_leaderboard_md(arena_df) | |
| gr.Markdown(md, elem_id="leaderboard_markdown") | |
| gr.Dataframe( | |
| headers=[ | |
| "Rank", | |
| "🤖 Model", | |
| "⭐ Arena Elo", | |
| "📊 95% CI", | |
| "🗳️ Votes", | |
| "Organization", | |
| "License", | |
| "Knowledge Cutoff", | |
| ], | |
| datatype=[ | |
| "str", | |
| "markdown", | |
| "number", | |
| "str", | |
| "number", | |
| "str", | |
| "str", | |
| "str", | |
| ], | |
| value=arena_table_vals, | |
| elem_id="arena_leaderboard_dataframe", | |
| height=700, | |
| column_widths=[50, 200, 120, 100, 100, 150, 150, 100], | |
| wrap=True, | |
| ) | |
| with gr.Tab("Full Leaderboard", id=1): | |
| md = make_full_leaderboard_md(elo_results) | |
| gr.Markdown(md, elem_id="leaderboard_markdown") | |
| full_table_vals = get_full_table(arena_df, model_table_df) | |
| gr.Dataframe( | |
| headers=[ | |
| "🤖 Model", | |
| "⭐ Arena Elo", | |
| "📈 MT-bench", | |
| "📚 MMLU", | |
| "Organization", | |
| "License", | |
| ], | |
| datatype=["markdown", "number", "number", "number", "str", "str"], | |
| value=full_table_vals, | |
| elem_id="full_leaderboard_dataframe", | |
| column_widths=[200, 100, 100, 100, 150, 150], | |
| height=700, | |
| wrap=True, | |
| ) | |
| if not show_plot: | |
| gr.Markdown( | |
| """ ## Visit our [HF space](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) for more analysis! | |
| If you want to see more models, please help us [add them](https://github.com/lm-sys/FastChat/blob/main/docs/arena.md#how-to-add-a-new-model). | |
| """, | |
| elem_id="leaderboard_markdown", | |
| ) | |
| else: | |
| pass | |
| gr.Markdown( | |
| f"""Note: we take the 95% confidence interval into account when determining a model's ranking. | |
| A model is ranked higher only if its lower bound of model score is higher than the upper bound of the other model's score. | |
| See Figure 3 below for visualization of the confidence intervals. | |
| """, | |
| elem_id="leaderboard_markdown" | |
| ) | |
| leader_component_values[:] = [default_md, p1, p2, p3, p4] | |
| if show_plot: | |
| gr.Markdown( | |
| f"""## More Statistics for Chatbot Arena\n | |
| Below are figures for more statistics. The code for generating them is also included in this [notebook]({notebook_url}). | |
| You can find more discussions in this blog [post](https://lmsys.org/blog/2023-12-07-leaderboard/). | |
| """, | |
| elem_id="leaderboard_markdown" | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown( | |
| "#### Figure 1: Fraction of Model A Wins for All Non-tied A vs. B Battles" | |
| ) | |
| plot_1 = gr.Plot(p1, show_label=False) | |
| with gr.Column(): | |
| gr.Markdown( | |
| "#### Figure 2: Battle Count for Each Combination of Models (without Ties)" | |
| ) | |
| plot_2 = gr.Plot(p2, show_label=False) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown( | |
| "#### Figure 3: Confidence Intervals on Model Strength (via Bootstrapping)" | |
| ) | |
| plot_3 = gr.Plot(p3, show_label=False) | |
| with gr.Column(): | |
| gr.Markdown( | |
| "#### Figure 4: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)" | |
| ) | |
| plot_4 = gr.Plot(p4, show_label=False) | |
| with gr.Accordion( | |
| "📝 Citation", | |
| open=True, | |
| ): | |
| citation_md = """ | |
| ### Citation | |
| Please cite the following paper if you find our leaderboard or dataset helpful. | |
| ``` | |
| @misc{chiang2024chatbot, | |
| title={Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference}, | |
| author={Wei-Lin Chiang and Lianmin Zheng and Ying Sheng and Anastasios Nikolas Angelopoulos and Tianle Li and Dacheng Li and Hao Zhang and Banghua Zhu and Michael Jordan and Joseph E. Gonzalez and Ion Stoica}, | |
| year={2024}, | |
| eprint={2403.04132}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.AI} | |
| } | |
| """ | |
| gr.Markdown(citation_md, elem_id="leaderboard_markdown") | |
| gr.Markdown(acknowledgment_md) | |
| if show_plot: | |
| return [md_1, plot_1, plot_2, plot_3, plot_4] | |
| return [md_1] | |
| block_css = """ | |
| #notice_markdown { | |
| font-size: 104% | |
| } | |
| #notice_markdown th { | |
| display: none; | |
| } | |
| #notice_markdown td { | |
| padding-top: 6px; | |
| padding-bottom: 6px; | |
| } | |
| #leaderboard_markdown { | |
| font-size: 104% | |
| } | |
| #leaderboard_markdown td { | |
| padding-top: 6px; | |
| padding-bottom: 6px; | |
| } | |
| #leaderboard_dataframe td { | |
| line-height: 0.1em; | |
| } | |
| footer { | |
| display:none !important | |
| } | |
| .sponsor-image-about img { | |
| margin: 0 20px; | |
| margin-top: 20px; | |
| height: 40px; | |
| max-height: 100%; | |
| width: auto; | |
| float: left; | |
| } | |
| """ | |
| acknowledgment_md = """ | |
| ### Acknowledgment | |
| We thank [Kaggle](https://www.kaggle.com/), [MBZUAI](https://mbzuai.ac.ae/), [a16z](https://www.a16z.com/), [Together AI](https://www.together.ai/), [Anyscale](https://www.anyscale.com/), [HuggingFace](https://huggingface.co/) for their generous [sponsorship](https://lmsys.org/donations/). | |
| <div class="sponsor-image-about"> | |
| <img src="https://storage.googleapis.com/public-arena-asset/kaggle.png" alt="Kaggle"> | |
| <img src="https://storage.googleapis.com/public-arena-asset/mbzuai.jpeg" alt="MBZUAI"> | |
| <img src="https://storage.googleapis.com/public-arena-asset/a16z.jpeg" alt="a16z"> | |
| <img src="https://storage.googleapis.com/public-arena-asset/together.png" alt="Together AI"> | |
| <img src="https://storage.googleapis.com/public-arena-asset/anyscale.png" alt="AnyScale"> | |
| <img src="https://storage.googleapis.com/public-arena-asset/huggingface.png" alt="HuggingFace"> | |
| </div> | |
| """ | |
| def build_demo(elo_results_file, leaderboard_table_file): | |
| text_size = gr.themes.sizes.text_lg | |
| with gr.Blocks( | |
| title="Chatbot Arena Leaderboard", | |
| theme=gr.themes.Base(text_size=text_size), | |
| css=block_css, | |
| ) as demo: | |
| leader_components = build_leaderboard_tab( | |
| elo_results_file, leaderboard_table_file, show_plot=True | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--share", action="store_true") | |
| args = parser.parse_args() | |
| elo_result_files = glob.glob("elo_results_*.pkl") | |
| elo_result_files.sort(key=lambda x: int(x[12:-4])) | |
| elo_result_file = elo_result_files[-1] | |
| leaderboard_table_files = glob.glob("leaderboard_table_*.csv") | |
| leaderboard_table_files.sort(key=lambda x: int(x[18:-4])) | |
| leaderboard_table_file = leaderboard_table_files[-1] | |
| demo = build_demo(elo_result_file, leaderboard_table_file) | |
| demo.launch(share=args.share) | |