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Restarting
on
CPU Upgrade
Nathan Habib
commited on
Commit
•
e3a8804
1
Parent(s):
a44ac97
add precision selector
Browse files
app.py
CHANGED
@@ -112,6 +112,8 @@ leaderboard_df = original_df.copy()
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pending_eval_queue_df,
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) = get_evaluation_queue_df(eval_queue, eval_queue_private, EVAL_REQUESTS_PATH, EVAL_COLS)
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## INTERACTION FUNCTIONS
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def add_new_eval(
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@@ -214,8 +216,8 @@ def change_tab(query_param: str):
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# Searching and filtering
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-
def update_table(hidden_df: pd.DataFrame, current_columns_df: pd.DataFrame, columns: list, type_query: list, size_query: list, show_deleted: bool, query: str):
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filtered_df = filter_models(hidden_df, type_query, size_query, show_deleted)
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if query != "":
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filtered_df = search_table(filtered_df, query)
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df = select_columns(filtered_df, columns)
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@@ -247,16 +249,17 @@ NUMERIC_INTERVALS = {
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}
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def filter_models(
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df: pd.DataFrame, type_query: list, size_query: list, show_deleted: bool
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) -> pd.DataFrame:
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# Show all models
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if show_deleted:
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filtered_df = df
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else: # Show only still on the hub models
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filtered_df = df[df[AutoEvalColumn.still_on_hub.name]
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type_emoji = [t[0] for t in type_query]
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filtered_df = filtered_df[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
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numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
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params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
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@@ -275,6 +278,12 @@ with demo:
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with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
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with gr.Row():
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with gr.Column():
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with gr.Row():
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shown_columns = gr.CheckboxGroup(
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choices=[
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@@ -308,11 +317,6 @@ with demo:
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value=True, label="Show gated/private/deleted models", interactive=True
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)
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with gr.Column(min_width=320):
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search_bar = gr.Textbox(
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placeholder="🔍 Search for your model and press ENTER...",
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show_label=False,
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elem_id="search-bar",
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)
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with gr.Box(elem_id="box-filter"):
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filter_columns_type = gr.CheckboxGroup(
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label="Model types",
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@@ -331,6 +335,13 @@ with demo:
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interactive=True,
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elem_id="filter-columns-type",
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)
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filter_columns_size = gr.CheckboxGroup(
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label="Model sizes",
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choices=list(NUMERIC_INTERVALS.keys()),
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@@ -373,6 +384,7 @@ with demo:
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_size,
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deleted_models_visibility,
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search_bar,
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@@ -386,6 +398,7 @@ with demo:
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_size,
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deleted_models_visibility,
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search_bar,
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@@ -400,6 +413,22 @@ with demo:
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_size,
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deleted_models_visibility,
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search_bar,
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@@ -414,6 +443,7 @@ with demo:
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_size,
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deleted_models_visibility,
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search_bar,
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@@ -428,6 +458,7 @@ with demo:
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_size,
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deleted_models_visibility,
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search_bar,
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pending_eval_queue_df,
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) = get_evaluation_queue_df(eval_queue, eval_queue_private, EVAL_REQUESTS_PATH, EVAL_COLS)
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print(leaderboard_df["Precision"].unique())
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## INTERACTION FUNCTIONS
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def add_new_eval(
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# Searching and filtering
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def update_table(hidden_df: pd.DataFrame, current_columns_df: pd.DataFrame, columns: list, type_query: list, precision_query: str, size_query: list, show_deleted: bool, query: str):
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filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
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if query != "":
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filtered_df = search_table(filtered_df, query)
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df = select_columns(filtered_df, columns)
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}
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def filter_models(
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df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
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) -> pd.DataFrame:
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# Show all models
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if show_deleted:
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filtered_df = df
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else: # Show only still on the hub models
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filtered_df = df[df[AutoEvalColumn.still_on_hub.name] is True]
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type_emoji = [t[0] for t in type_query]
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filtered_df = filtered_df[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
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filtered_df = filtered_df[df[AutoEvalColumn.precision.name].isin(precision_query)]
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numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
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params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
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with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
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with gr.Row():
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with gr.Column():
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with gr.Row():
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search_bar = gr.Textbox(
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placeholder=" 🔍 Search for your model and press ENTER...",
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show_label=False,
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elem_id="search-bar",
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)
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with gr.Row():
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shown_columns = gr.CheckboxGroup(
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choices=[
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value=True, label="Show gated/private/deleted models", interactive=True
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)
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with gr.Column(min_width=320):
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with gr.Box(elem_id="box-filter"):
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filter_columns_type = gr.CheckboxGroup(
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label="Model types",
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interactive=True,
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elem_id="filter-columns-type",
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)
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filter_columns_precision = gr.CheckboxGroup(
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label="Precision",
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choices=["torch.float16", "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"],
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value=["torch.float16", "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"],
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interactive=True,
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elem_id="filter-columns-precision",
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)
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filter_columns_size = gr.CheckboxGroup(
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label="Model sizes",
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choices=list(NUMERIC_INTERVALS.keys()),
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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filter_columns_size,
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deleted_models_visibility,
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search_bar,
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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filter_columns_size,
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deleted_models_visibility,
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search_bar,
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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filter_columns_size,
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deleted_models_visibility,
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search_bar,
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],
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leaderboard_table,
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queue=True,
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)
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filter_columns_precision.change(
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update_table,
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[
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hidden_leaderboard_table_for_search,
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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filter_columns_size,
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deleted_models_visibility,
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search_bar,
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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filter_columns_size,
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deleted_models_visibility,
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search_bar,
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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filter_columns_size,
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deleted_models_visibility,
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search_bar,
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