Clémentine
added automatic update of the best LLM models
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import json
import os
import re
from datetime import datetime, timezone
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import HfApi, snapshot_download
from src.assets.css_html_js import custom_css, get_window_url_params
from src.assets.text_content import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
)
from src.display_models.get_model_metadata import DO_NOT_SUBMIT_MODELS, ModelType
from src.display_models.modelcard_filter import check_model_card
from src.display_models.utils import (
AutoEvalColumn,
EvalQueueColumn,
fields,
styled_error,
styled_message,
styled_warning,
)
from src.manage_collections import update_collections
from src.load_from_hub import get_all_requested_models, get_evaluation_queue_df, get_leaderboard_df, is_model_on_hub
from src.rate_limiting import user_submission_permission
pd.set_option("display.precision", 1)
# clone / pull the lmeh eval data
H4_TOKEN = os.environ.get("H4_TOKEN", None)
QUEUE_REPO = "open-llm-leaderboard/requests"
RESULTS_REPO = "open-llm-leaderboard/results"
PRIVATE_QUEUE_REPO = "open-llm-leaderboard/private-requests"
PRIVATE_RESULTS_REPO = "open-llm-leaderboard/private-results"
IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True))
EVAL_REQUESTS_PATH = "eval-queue"
EVAL_RESULTS_PATH = "eval-results"
EVAL_REQUESTS_PATH_PRIVATE = "eval-queue-private"
EVAL_RESULTS_PATH_PRIVATE = "eval-results-private"
api = HfApi(token=H4_TOKEN)
def restart_space():
api.restart_space(repo_id="HuggingFaceH4/open_llm_leaderboard", token=H4_TOKEN)
# Rate limit variables
RATE_LIMIT_PERIOD = 7
RATE_LIMIT_QUOTA = 5
# Column selection
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
if not IS_PUBLIC:
COLS.insert(2, AutoEvalColumn.precision.name)
TYPES.insert(2, AutoEvalColumn.precision.type)
EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
BENCHMARK_COLS = [
c.name
for c in [
AutoEvalColumn.arc,
AutoEvalColumn.hellaswag,
AutoEvalColumn.mmlu,
AutoEvalColumn.truthfulqa,
]
]
snapshot_download(repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None)
snapshot_download(repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None)
requested_models, users_to_submission_dates = get_all_requested_models(EVAL_REQUESTS_PATH)
original_df = get_leaderboard_df(EVAL_RESULTS_PATH, COLS, BENCHMARK_COLS)
update_collections(original_df.copy())
leaderboard_df = original_df.copy()
models = original_df["model_name_for_query"].tolist() # needed for model backlinks in their to the leaderboard
to_be_dumped = f"models = {repr(models)}\n"
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
## INTERACTION FUNCTIONS
def add_new_eval(
model: str,
base_model: str,
revision: str,
precision: str,
private: bool,
weight_type: str,
model_type: str,
):
precision = precision.split(" ")[0]
current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
if model_type is None or model_type == "":
return styled_error("Please select a model type.")
# Is the user rate limited?
num_models_submitted_in_period = user_submission_permission(model, users_to_submission_dates, RATE_LIMIT_PERIOD)
if num_models_submitted_in_period > RATE_LIMIT_QUOTA:
error_msg = f"Organisation or user `{model.split('/')[0]}`"
error_msg += f"already has {num_models_submitted_in_period} model requests submitted to the leaderboard "
error_msg += f"in the last {RATE_LIMIT_PERIOD} days.\n"
error_msg += (
"Please wait a couple of days before resubmitting, so that everybody can enjoy using the leaderboard 🤗"
)
return styled_error(error_msg)
# Did the model authors forbid its submission to the leaderboard?
if model in DO_NOT_SUBMIT_MODELS or base_model in DO_NOT_SUBMIT_MODELS:
return styled_warning("Model authors have requested that their model be not submitted on the leaderboard.")
# Does the model actually exist?
if revision == "":
revision = "main"
if weight_type in ["Delta", "Adapter"]:
base_model_on_hub, error = is_model_on_hub(base_model, revision)
if not base_model_on_hub:
return styled_error(f'Base model "{base_model}" {error}')
if not weight_type == "Adapter":
model_on_hub, error = is_model_on_hub(model, revision)
if not model_on_hub:
return styled_error(f'Model "{model}" {error}')
model_info = api.model_info(repo_id=model, revision=revision)
size_pattern = size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
try:
model_size = round(model_info.safetensors["total"] / 1e9, 3)
except AttributeError:
try:
size_match = re.search(size_pattern, model.lower())
model_size = size_match.group(0)
model_size = round(float(model_size[:-1]) if model_size[-1] == "b" else float(model_size[:-1]) / 1e3, 3)
except AttributeError:
return 65
size_factor = 8 if (precision == "GPTQ" or "GPTQ" in model) else 1
model_size = size_factor * model_size
try:
license = model_info.cardData["license"]
except Exception:
license = "?"
# Were the model card and license filled?
modelcard_OK, error_msg = check_model_card(model)
if not modelcard_OK:
return styled_error(error_msg)
# Seems good, creating the eval
print("Adding new eval")
eval_entry = {
"model": model,
"base_model": base_model,
"revision": revision,
"private": private,
"precision": precision,
"weight_type": weight_type,
"status": "PENDING",
"submitted_time": current_time,
"model_type": model_type,
"likes": model_info.likes,
"params": model_size,
"license": license,
}
user_name = ""
model_path = model
if "/" in model:
user_name = model.split("/")[0]
model_path = model.split("/")[1]
print("Creating eval file")
OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
os.makedirs(OUT_DIR, exist_ok=True)
out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{precision}_{weight_type}.json"
# Check for duplicate submission
if f"{model}_{revision}_{precision}" in requested_models:
return styled_warning("This model has been already submitted.")
with open(out_path, "w") as f:
f.write(json.dumps(eval_entry))
print("Uploading eval file")
api.upload_file(
path_or_fileobj=out_path,
path_in_repo=out_path.split("eval-queue/")[1],
repo_id=QUEUE_REPO,
repo_type="dataset",
commit_message=f"Add {model} to eval queue",
)
# Remove the local file
os.remove(out_path)
return styled_message(
"Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list."
)
# Basics
def change_tab(query_param: str):
query_param = query_param.replace("'", '"')
query_param = json.loads(query_param)
if isinstance(query_param, dict) and "tab" in query_param and query_param["tab"] == "evaluation":
return gr.Tabs.update(selected=1)
else:
return gr.Tabs.update(selected=0)
# 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.dummy.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] + [AutoEvalColumn.dummy.name]
]
return filtered_df
NUMERIC_INTERVALS = {
"?": pd.Interval(-1, 0, closed="right"),
"0~1.5": pd.Interval(0, 1.5, closed="right"),
"1.5~3": pd.Interval(1.5, 3, closed="right"),
"3~7": pd.Interval(3, 7, closed="right"),
"7~13": pd.Interval(7, 13, closed="right"),
"13~35": pd.Interval(13, 35, closed="right"),
"35~60": pd.Interval(35, 60, closed="right"),
"60+": pd.Interval(60, 10000, closed="right"),
}
def filter_queries(query: str, filtered_df: pd.DataFrame):
"""Added by Abishek"""
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[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
filtered_df = filtered_df[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
for c in COLS
if c
not in [
AutoEvalColumn.dummy.name,
AutoEvalColumn.model.name,
AutoEvalColumn.model_type_symbol.name,
AutoEvalColumn.still_on_hub.name,
]
],
value=[
c
for c in COLS_LITE
if c
not in [
AutoEvalColumn.dummy.name,
AutoEvalColumn.model.name,
AutoEvalColumn.model_type_symbol.name,
AutoEvalColumn.still_on_hub.name,
]
],
label="Select columns to show",
elem_id="column-select",
interactive=True,
)
with gr.Row():
deleted_models_visibility = gr.Checkbox(
value=True, 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=[
ModelType.PT.to_str(),
ModelType.FT.to_str(),
ModelType.IFT.to_str(),
ModelType.RL.to_str(),
ModelType.Unknown.to_str(),
],
value=[
ModelType.PT.to_str(),
ModelType.FT.to_str(),
ModelType.IFT.to_str(),
ModelType.RL.to_str(),
ModelType.Unknown.to_str(),
],
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_precision = gr.CheckboxGroup(
label="Precision",
choices=["torch.float16", "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"],
value=["torch.float16", "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"],
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[
[AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name]
+ shown_columns.value
+ [AutoEvalColumn.dummy.name]
],
headers=[
AutoEvalColumn.model_type_symbol.name,
AutoEvalColumn.model.name,
]
+ shown_columns.value
+ [AutoEvalColumn.dummy.name],
datatype=TYPES,
max_rows=None,
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,
headers=COLS,
datatype=TYPES,
max_rows=None,
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,
)
shown_columns.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,
)
filter_columns_type.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,
)
filter_columns_precision.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,
)
filter_columns_size.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,
)
deleted_models_visibility.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,
max_rows=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,
max_rows=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,
max_rows=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", placeholder="main")
private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC)
model_type = gr.Dropdown(
choices=[
ModelType.PT.to_str(" : "),
ModelType.FT.to_str(" : "),
ModelType.IFT.to_str(" : "),
ModelType.RL.to_str(" : "),
],
label="Model type",
multiselect=False,
value=None,
interactive=True,
)
with gr.Column():
precision = gr.Dropdown(
choices=["float16", "bfloat16", "8bit (LLM.int8)", "4bit (QLoRA / FP4)", "GPTQ"],
label="Precision",
multiselect=False,
value="float16",
interactive=True,
)
weight_type = gr.Dropdown(
choices=["Original", "Delta", "Adapter"],
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,
private,
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,
)
dummy = gr.Textbox(visible=False)
demo.load(
change_tab,
dummy,
tabs,
_js=get_window_url_params,
)
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(concurrency_count=40).launch()