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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. Contribute your vote 🗳️ at [chat.lmsys.org](https://chat.lmsys.org)! | |
""" | |
return leaderboard_md | |
def make_arena_leaderboard_md(arena_df, arena_subset_df=None, name="Overall"): | |
total_votes = sum(arena_df["num_battles"]) // 2 | |
total_models = len(arena_df) | |
space = " " | |
if arena_subset_df is not None: | |
total_subset_votes = sum(arena_subset_df["num_battles"]) // 2 | |
total_subset_models = len(arena_subset_df) | |
vote_str = f"{space} {name} #models: **{total_subset_models}**.{space} {name} #votes: **{'{:,}'.format(total_subset_votes)}**." | |
else: | |
vote_str = "" | |
leaderboard_md = f""" | |
Total #models: **{total_models}**.{space} Total #votes: **{"{:,}".format(total_votes)}**.{vote_str}{space} Last updated: March 29, 2024. | |
**NEW!** Click the buttons below to view the ELO leaderboard and stats for different input categories. You are currently viewing **{name}** inputs. | |
""" | |
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 create_ranking_str(ranking, ranking_difference): | |
if ranking_difference > 0: | |
return f"{int(ranking)} (\u2191{int(ranking_difference)})" | |
elif ranking_difference < 0: | |
return f"{int(ranking)} (\u2193{int(-ranking_difference)})" | |
else: | |
return f"{int(ranking)}" | |
def get_arena_table(arena_df, model_table_df, arena_subset_df=None): | |
arena_df = arena_df.sort_values(by=["rating"], ascending=False) | |
arena_df = arena_df.sort_values(by=["final_ranking"], ascending=True) | |
# sort by rating | |
if arena_subset_df is not None: | |
arena_subset_df = arena_subset_df.sort_values(by=["rating"], ascending=False) | |
arena_subset_df = arena_subset_df.sort_values(by=["final_ranking"], ascending=True) | |
# join arena_df and arena_subset_df on index | |
arena_df = arena_subset_df.join(arena_df["final_ranking"], rsuffix="_global", how="inner") | |
arena_df['ranking_difference'] = arena_df['final_ranking_global'] - arena_df['final_ranking'] | |
arena_df["final_ranking"] = arena_df.apply(lambda x: create_ranking_str(x["final_ranking"], x["ranking_difference"]), axis=1) | |
values = [] | |
for i in range(len(arena_df)): | |
row = [] | |
model_key = arena_df.index[i] | |
try: | |
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) | |
except Exception as e: | |
print(f"{model_key} - {e}") | |
return values | |
def update_leaderboard_and_plots(button, arena_df, model_table_df, arena_subset_df, elo_subset_results): | |
arena_values = get_arena_table(arena_df, model_table_df, arena_subset_df) | |
p1 = elo_subset_results["win_fraction_heatmap"] | |
p2 = elo_subset_results["battle_count_heatmap"] | |
p3 = elo_subset_results["bootstrap_elo_rating"] | |
p4 = elo_subset_results["average_win_rate_bar"] | |
more_stats_md = f"""More Statistics for Chatbot Arena ({button}) | |
""" | |
leaderboard_md = make_arena_leaderboard_md(arena_df, arena_subset_df, name=button) | |
return arena_values, p1, p2, p3, p4, more_stats_md, leaderboard_md | |
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_chinese_results = elo_results["chinese"] | |
elo_long_results = elo_results["long"] | |
elo_english_results = elo_results["english"] | |
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"] | |
arena_chinese_df = elo_chinese_results["leaderboard_table_df"] | |
arena_long_df = elo_long_results["leaderboard_table_df"] | |
arena_english_df = elo_english_results["leaderboard_table_df"] | |
default_md = make_default_md(arena_df, elo_results) | |
md_1 = gr.Markdown(default_md, elem_id="leaderboard_markdown") | |
# md = make_arena_leaderboard_md(arena_df, arena_chinese_df, arena_long_df, arena_english_df) | |
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) | |
leaderboard_markdown = gr.Markdown(md, elem_id="leaderboard_markdown") | |
with gr.Row(): | |
overall_rating = gr.Button("Overall") | |
# update_overall_rating_df = lambda _: get_arena_table(arena_df, model_table_df) | |
update_overall_rating_df = lambda x: update_leaderboard_and_plots(x, arena_df, model_table_df, None, elo_results) | |
english_rating = gr.Button("English") | |
update_english_rating_df = lambda x: update_leaderboard_and_plots(x, arena_df, model_table_df, arena_english_df, elo_english_results) | |
# update_english_rating_df = lambda _: get_arena_table(arena_df, model_table_df, arena_english_df) | |
chinese_rating = gr.Button("Chinese") | |
update_chinese_rating_df = lambda x: update_leaderboard_and_plots(x, arena_df, model_table_df, arena_chinese_df, elo_chinese_results) | |
# update_chinese_rating_df = lambda _: get_arena_table(arena_df, model_table_df, arena_chinese_df) | |
long_context_rating = gr.Button("Long Context") | |
update_long_context_rating_df = lambda x: update_leaderboard_and_plots(x, arena_df, model_table_df, arena_long_df, elo_long_results) | |
# update_long_context_rating_df = lambda _: get_arena_table(arena_df, model_table_df, arena_long_df) | |
elo_display_df = 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=[70, 190, 110, 100, 90, 160, 150, 140], | |
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. Code to recreate these tables and plots in this [notebook]({notebook_url}) and more discussions in this blog [post](https://lmsys.org/blog/2023-12-07-leaderboard/). | |
""", | |
elem_id="leaderboard_markdown" | |
) | |
leader_component_values[:] = [default_md, p1, p2, p3, p4] | |
if show_plot: | |
# more_stats_md = gr.Markdown( | |
# f"""## More Statistics for Chatbot Arena (Overall)""", | |
# elem_id="leaderboard_markdown" | |
# ) | |
more_stats_md = gr.Button("More Statistics for Chatbot Arena (Overall)", elem_id="non-interactive-button") | |
with gr.Row(): | |
with gr.Column(): | |
gr.Markdown( | |
"#### Figure 1: Fraction of Model A Wins for All Non-tied A vs. B Battles", elem_id="plot-title", variant="panel" | |
) | |
plot_1 = gr.Plot(p1, show_label=False, elem_id="plot-container") | |
with gr.Column(): | |
gr.Markdown( | |
"#### Figure 2: Battle Count for Each Combination of Models (without Ties)", elem_id="plot-title" | |
) | |
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)", elem_id="plot-title" | |
) | |
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)", elem_id="plot-title" | |
) | |
plot_4 = gr.Plot(p4, show_label=False) | |
overall_rating.click(fn=update_overall_rating_df, inputs=overall_rating, outputs=[elo_display_df, plot_1, plot_2, plot_3, plot_4, more_stats_md, leaderboard_markdown]) | |
english_rating.click(fn=update_english_rating_df, inputs=english_rating, outputs=[elo_display_df, plot_1, plot_2, plot_3, plot_4, more_stats_md, leaderboard_markdown]) | |
chinese_rating.click(fn=update_chinese_rating_df, inputs=chinese_rating ,outputs=[elo_display_df, plot_1, plot_2, plot_3, plot_4, more_stats_md, leaderboard_markdown]) | |
long_context_rating.click(fn=update_long_context_rating_df, inputs=long_context_rating, outputs=[elo_display_df, plot_1, plot_2, plot_3, plot_4, more_stats_md, 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; | |
} | |
#plot-title { | |
text-align: center; | |
display:block; | |
} | |
#non-interactive-button { | |
display: inline-block; | |
padding: 10px 10px; | |
background-color: #f7f7f7; /* Super light grey background */ | |
color: #000000; /* Black text */ | |
text-align: center; | |
font-size: 26px; /* Larger text */ | |
border-radius: 0; /* Straight edges, no border radius */ | |
border: 0px solid #dcdcdc; /* A light grey border to match the background */ | |
font-weight: bold; | |
user-select: none; /* The text inside the button is not selectable */ | |
pointer-events: none; /* The button is non-interactive */ | |
} | |
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 | |
theme = gr.themes.Base(text_size=text_size) | |
theme.set(button_secondary_background_fill_hover="*primary_300", button_secondary_background_fill_hover_dark="*primary_700") | |
with gr.Blocks( | |
title="Chatbot Arena Leaderboard", | |
theme=theme, | |
# theme = gr.themes.Base.load("theme.json"), | |
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) | |