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import gradio as gr
from src.utils import model_hyperlink, process_score
LEADERBOARD_COLUMN_TO_DATATYPE = {
# open llm
"Model 🤗": "markdown",
"Experiment 🧪": "str",
# primary measurements
"Prefill (s)": "number",
"Decode (tokens/s)": "number",
"Memory (MB)": "number",
"Energy (tokens/kWh)": "number",
# deployment settings
"DType 📥": "str",
"Backend 🏭": "str",
"Optimization 🛠️": "str",
"Quantization 🗜️": "str",
# additional measurements
"Architecture 🏛️": "markdown",
"Params (B)": "number",
"Open LLM Score (%)": "number",
"End-to-End (s)": "number",
"End-to-End (tokens/s)": "number",
"Reserved Memory (MB)": "number",
"Used Memory (MB)": "number",
}
PRIMARY_COLUMNS = [
"Model 🤗",
"Experiment 🧪",
"Prefill (s)",
"Decode (tokens/s)",
"Memory (MB)",
"Energy (tokens/kWh)",
"Open LLM Score (%)",
]
def process_model(model_name):
link = f"https://huggingface.co/{model_name}"
return model_hyperlink(link, model_name)
def get_leaderboard_df(llm_perf_df):
df = llm_perf_df.copy()
# transform for leaderboard
df["Model 🤗"] = df["Model 🤗"].apply(process_model)
# process quantization for leaderboard
df["Open LLM Score (%)"] = df.apply(
lambda x: process_score(x["Open LLM Score (%)"], x["Quantization 🗜️"]),
axis=1,
)
return df
def create_leaderboard_table(llm_perf_df):
# get dataframe
leaderboard_df = get_leaderboard_df(llm_perf_df)
# create search bar
with gr.Row():
search_bar = gr.Textbox(
label="Model 🤗",
info="🔍 Search for a model name",
elem_id="search-bar",
)
# create checkboxes
with gr.Row():
columns_checkboxes = gr.CheckboxGroup(
label="Columns 📊",
value=PRIMARY_COLUMNS,
choices=list(LEADERBOARD_COLUMN_TO_DATATYPE.keys()),
info="☑️ Select the columns to display",
elem_id="columns-checkboxes",
)
# create table
leaderboard_table = gr.components.Dataframe(
value=leaderboard_df[PRIMARY_COLUMNS],
datatype=list(LEADERBOARD_COLUMN_TO_DATATYPE.values()),
headers=list(LEADERBOARD_COLUMN_TO_DATATYPE.keys()),
elem_id="leaderboard-table",
)
return search_bar, columns_checkboxes, leaderboard_table
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