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import subprocess
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from src.about import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
BENCHMARK_COLS,
COLS,
EVAL_COLS,
EVAL_TYPES,
NUMERIC_INTERVALS,
TYPES,
AutoEvalColumn,
ModelType,
fields,
WeightType,
Precision
)
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval
def restart_space():
API.restart_space(repo_id=REPO_ID)
try:
print(EVAL_REQUESTS_PATH)
snapshot_download(
repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
)
except Exception:
restart_space()
try:
print(EVAL_RESULTS_PATH)
snapshot_download(
repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
)
except Exception:
restart_space()
raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
leaderboard_df = original_df.copy()
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
# Searching and filtering
def update_table(
hidden_df: pd.DataFrame,
columns: list,
type_query: list,
precision_query: str,
size_query: list,
show_deleted: bool,
query: str,
):
filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
filtered_df = filter_queries(query, filtered_df)
df = select_columns(filtered_df, columns)
return df
def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
return df[(df[AutoEvalColumn.model.name].str.contains(query, case=False))]
def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
always_here_cols = [
AutoEvalColumn.model_type_symbol.name,
AutoEvalColumn.model.name,
]
# We use COLS to maintain sorting
filtered_df = df[
always_here_cols + [c for c in COLS if c in df.columns and c in columns]
]
return filtered_df
def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
final_df = []
if query != "":
queries = [q.strip() for q in query.split(";")]
for _q in queries:
_q = _q.strip()
if _q != "":
temp_filtered_df = search_table(filtered_df, _q)
if len(temp_filtered_df) > 0:
final_df.append(temp_filtered_df)
if len(final_df) > 0:
filtered_df = pd.concat(final_df)
filtered_df = filtered_df.drop_duplicates(
subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
)
return filtered_df
def filter_models(
df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
) -> pd.DataFrame:
# Show all models
if show_deleted:
filtered_df = df
else: # Show only still on the hub models
filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
type_emoji = [t[0] for t in type_query]
filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
filtered_df = filtered_df.loc[mask]
return filtered_df
demo = gr.Blocks(css=custom_css)
with demo:
gr.HTML(TITLE)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(
placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
show_label=False,
elem_id="search-bar",
)
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices=[
c.name
for c in fields(AutoEvalColumn)
if not c.hidden and not c.never_hidden
],
value=[
c.name
for c in fields(AutoEvalColumn)
if c.displayed_by_default and not c.hidden and not c.never_hidden
],
label="Select columns to show",
elem_id="column-select",
interactive=True,
)
with gr.Row():
deleted_models_visibility = gr.Checkbox(
value=False, label="Show gated/private/deleted models", interactive=True
)
with gr.Column(min_width=320):
#with gr.Box(elem_id="box-filter"):
filter_columns_type = gr.CheckboxGroup(
label="Model types",
choices=[t.to_str() for t in ModelType],
value=[t.to_str() for t in ModelType],
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_precision = gr.CheckboxGroup(
label="Precision",
choices=[i.value.name for i in Precision],
value=[i.value.name for i in Precision],
interactive=True,
elem_id="filter-columns-precision",
)
filter_columns_size = gr.CheckboxGroup(
label="Model sizes (in billions of parameters)",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_id="filter-columns-size",
)
leaderboard_table = gr.components.Dataframe(
value=leaderboard_df[
[c.name for c in fields(AutoEvalColumn) if c.never_hidden]
+ shown_columns.value
],
headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
datatype=TYPES,
elem_id="leaderboard-table",
interactive=False,
visible=True,
)
# Dummy leaderboard for handling the case when the user uses backspace key
hidden_leaderboard_table_for_search = gr.components.Dataframe(
value=original_df[COLS],
headers=COLS,
datatype=TYPES,
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
deleted_models_visibility,
search_bar,
],
leaderboard_table,
)
for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
deleted_models_visibility,
search_bar,
],
leaderboard_table,
queue=True,
)
with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
with gr.Column():
with gr.Row():
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
with gr.Column():
with gr.Accordion(
f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
open=False,
):
with gr.Row():
finished_eval_table = gr.components.Dataframe(
value=finished_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5,
)
with gr.Accordion(
f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
open=False,
):
with gr.Row():
running_eval_table = gr.components.Dataframe(
value=running_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5,
)
with gr.Accordion(
f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
open=False,
):
with gr.Row():
pending_eval_table = gr.components.Dataframe(
value=pending_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5,
)
with gr.Row():
gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
with gr.Row():
with gr.Column():
model_name_textbox = gr.Textbox(label="Model name")
revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
model_type = gr.Dropdown(
choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
label="Model type",
multiselect=False,
value=None,
interactive=True,
)
with gr.Column():
precision = gr.Dropdown(
choices=[i.value.name for i in Precision if i != Precision.Unknown],
label="Precision",
multiselect=False,
value="float16",
interactive=True,
)
weight_type = gr.Dropdown(
choices=[i.value.name for i in WeightType],
label="Weights type",
multiselect=False,
value="Original",
interactive=True,
)
base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
submit_button = gr.Button("Submit Eval")
submission_result = gr.Markdown()
submit_button.click(
add_new_eval,
[
model_name_textbox,
base_model_name_textbox,
revision_name_textbox,
precision,
weight_type,
model_type,
],
submission_result,
)
with gr.Row():
with gr.Accordion("📙 Citation", open=False):
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
lines=20,
elem_id="citation-button",
show_copy_button=True,
)
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()
# import gradio as gr
# import pandas as pd
# csv_filename = 'leaderboard.csv'
# # url = 'https://docs.google.com/spreadsheets/d/1Oh3nrbdWjKuh9twJsc9yJLppiJeD_BZyKgCTOxRkALM/export?format=csv'
# def get_data_classifica():
# dataset = pd.read_csv("leaderboard_general.csv", sep=',')
# if 'model ' in dataset.columns:
# dataset.rename(columns={'model ': 'model'}, inplace=True)
# df_classifica = dataset[['model', 'helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']]
# df_classifica['media'] = df_classifica[['helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']].mean(axis=1)
# df_classifica['media'] = df_classifica['media'].round(3)
# df_classifica = df_classifica.sort_values(by='media', ascending=False)
# df_classifica = df_classifica[['model', 'media', 'helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']]
# return df_classifica
# def get_data_totale():
# dataset = pd.read_csv("leaderboard_general.csv", sep=',')
# if 'model ' in dataset.columns:
# dataset.rename(columns={'model ': 'model'}, inplace=True)
# return dataset
# with gr.Blocks() as demo:
# with gr.Tab('Classifica Generale'):
# gr.Markdown('''# Classifica generale degli LLM italiani''')
# discord_link = 'https://discord.gg/m7sS3mduY2'
# gr.Markdown('''
# I modelli sottostanti sono stati testati con [lm_evaluation_harness](https://github.com/EleutherAI/lm-evaluation-harness) su task specifici per l'italiano introdotti con questa [PR](https://github.com/EleutherAI/lm-evaluation-harness/pull/1358).
# L'intero progetto, i modelli e i dataset sono rigorosamente open source e tutti i risultati sono riproducibili lanciando i seguenti comandi:
# ```
# lm_eval --model hf --model_args pretrained=HUGGINGFACE_MODEL_ID --tasks hellaswag_it,arc_it --device cuda:0 --batch_size auto:2
# ```
# ```
# lm_eval --model hf --model_args pretrained=HUGGINGFACE_MODEL_ID --tasks m_mmlu_it --num_fewshot 5 --device cuda:0 --batch_size auto:2
# ```
# ''')
# gr.DataFrame(get_data_classifica, every=3600)
# gr.Markdown(f"Contributore principale: @giux78")
# gr.Markdown('''
# ### Risultati su modelli "internazionali" (instruct)
# | Model | Arc-c | HellaS | MMUL | AVG |
# | --- | --- | --- | --- | --- |
# | Mixtral 8x22b | 55.3 | 77.1 | 75.8 | 69.4 |
# | LLama3 70b | 52.9 | 70.3 | 74.8 | 66.0 |
# | command-r-plus | 49.5 | 74.9 | 67.6 | 64.0 |
# | Mixtral 8x7b | 51.1 | 72.9 | 65.9 | 63.3 |
# | LLama2 70b | 49.4 | 70.9 | 65.1 | 61.8 |
# | command-r-v01 | 50.8 | 72.3 | 60.0 | 61.0 |
# | Phi-3-mini | 43.46 | 61.44 | 56.55 | 53.8 |
# | LLama3 8b | 44.3 | 59.9 | 55.7 | 53.3 |
# | LLama1 34b | 42.9 | 65.4 | 49.0 | 52.4 |
# | Mistral 7b | 41.49 | 61.22 | 52.53 | 51.7 |
# | Gemma 1.1 7b | 41.75 | 54.07 | 49.45 | 48.4 |
# ''')
# with gr.Tab('Classifica RAG'):
# gr.Markdown('''# Classifica RAG degli LLM italiani''')
# gr.Markdown(f'''In questa sezione i modelli sono valutati su dei task di Q&A e ordinati per F1 Score e EM (Exact Match). La repo di riferimento è [questa](https://github.com/C080/open-llm-ita-leaderboard).
# I modelli in cima alla classifica sono ritenuti preferibili per i task di Retrieval Augmented Generation.''')
# gr.Dataframe(pd.read_csv(csv_filename, sep=';'))
# gr.Markdown(f"Si ringrazia il @galatolo per il codice dell'eval.")
# with gr.Tab('Eval aggiuntive'):
# gr.Markdown('''# Altre evaluation''')
# gr.Markdown('''Qui ci sono altri test di altri modelli, che non sono ancora stati integrati nella classifica generale.''')
# gr.DataFrame(get_data_totale, every=3600)
# with gr.Tab('Informazioni'):
# form_link = "https://forms.gle/Gc9Dfu52xSBhQPpAA"
# gr.Markdown('''# Community discord
# Se vuoi contribuire al progetto o semplicemente unirti alla community di LLM italiani unisciti al nostro [discord!](https://discord.gg/m7sS3mduY2)
# # Aggiungi il tuo modello
# Se hai sviluppato un tuo modello che vuoi far valutare, compila il form [qui](https://forms.gle/Gc9Dfu52xSBhQPpAA) è tutto gratuito!
# ''')
# with gr.Tab('Sponsor'):
# gr.Markdown('''
# # Sponsor
# Le evaluation della classifica generale sono state gentilmente offerte da un provider cloud italiano [seeweb.it](https://www.seeweb.it/) specializzato in servizi di GPU cloud e AI.
# ''')
# demo.launch() |