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Running
on
CPU Upgrade
Clémentine
commited on
Commit
·
b762711
1
Parent(s):
8f9bc6a
Added checkbox for merges
Browse files- app.py +14 -4
- src/display/utils.py +3 -3
- src/leaderboard/read_evals.py +1 -1
app.py
CHANGED
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@@ -78,10 +78,11 @@ def update_table(
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precision_query: str,
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size_query: list,
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show_deleted: bool,
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show_flagged: bool,
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query: str,
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):
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-
filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted, show_flagged)
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filtered_df = filter_queries(query, filtered_df)
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df = select_columns(filtered_df, columns)
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return df
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@@ -129,7 +130,7 @@ def filter_queries(query: str, filtered_df: pd.DataFrame):
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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, show_flagged: 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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@@ -137,6 +138,9 @@ def filter_models(
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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] == True]
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if not show_flagged:
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filtered_df = filtered_df[filtered_df[AutoEvalColumn.flagged.name] == False]
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@@ -151,7 +155,7 @@ def filter_models(
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return filtered_df
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-
leaderboard_df = filter_models(leaderboard_df, [t.to_str(" : ") for t in ModelType], list(NUMERIC_INTERVALS.keys()), [i.value.name for i in Precision], False, False)
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demo = gr.Blocks(css=custom_css)
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with demo:
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@@ -188,6 +192,9 @@ with demo:
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deleted_models_visibility = gr.Checkbox(
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value=False, label="Show private/deleted models", interactive=True
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)
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flagged_models_visibility = gr.Checkbox(
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value=False, label="Show flagged models", interactive=True
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)
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@@ -245,6 +252,7 @@ with demo:
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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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flagged_models_visibility,
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search_bar,
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],
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@@ -262,6 +270,7 @@ with demo:
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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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flagged_models_visibility,
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search_bar,
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],
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@@ -270,7 +279,7 @@ with demo:
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# Check query parameter once at startup and update search bar + hidden component
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demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar])
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-
for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility, flagged_models_visibility]:
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selector.change(
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update_table,
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[
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@@ -280,6 +289,7 @@ with demo:
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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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flagged_models_visibility,
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search_bar,
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],
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precision_query: str,
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size_query: list,
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show_deleted: bool,
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+
show_merges: bool,
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show_flagged: bool,
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query: str,
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):
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+
filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted, show_merges, show_flagged)
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filtered_df = filter_queries(query, filtered_df)
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df = select_columns(filtered_df, columns)
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return df
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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, show_merges: bool, show_flagged: 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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else: # Show only still on the hub models
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filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
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if not show_merges:
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filtered_df = filtered_df[filtered_df[AutoEvalColumn.merged.name] == False]
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+
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if not show_flagged:
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filtered_df = filtered_df[filtered_df[AutoEvalColumn.flagged.name] == False]
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return filtered_df
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+
leaderboard_df = filter_models(leaderboard_df, [t.to_str(" : ") for t in ModelType], list(NUMERIC_INTERVALS.keys()), [i.value.name for i in Precision], False, False, False)
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demo = gr.Blocks(css=custom_css)
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with demo:
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deleted_models_visibility = gr.Checkbox(
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value=False, label="Show private/deleted models", interactive=True
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)
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merged_models_visibility = gr.Checkbox(
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value=False, label="Show merges", interactive=True
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)
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flagged_models_visibility = gr.Checkbox(
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value=False, label="Show flagged models", interactive=True
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)
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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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+
merged_models_visibility,
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flagged_models_visibility,
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search_bar,
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],
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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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+
merged_models_visibility,
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flagged_models_visibility,
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search_bar,
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],
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# Check query parameter once at startup and update search bar + hidden component
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demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar])
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+
for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility, merged_models_visibility, flagged_models_visibility]:
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selector.change(
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update_table,
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[
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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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+
merged_models_visibility,
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flagged_models_visibility,
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search_bar,
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],
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src/display/utils.py
CHANGED
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@@ -46,7 +46,7 @@ auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type",
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auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
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auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
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auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
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-
auto_eval_column_dict.append(["
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auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
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auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
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auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
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@@ -73,7 +73,7 @@ baseline_row = {
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AutoEvalColumn.model.name: "<p>Baseline</p>",
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AutoEvalColumn.revision.name: "N/A",
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AutoEvalColumn.precision.name: None,
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-
AutoEvalColumn.
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AutoEvalColumn.average.name: 31.0,
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AutoEvalColumn.arc.name: 25.0,
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AutoEvalColumn.hellaswag.name: 25.0,
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@@ -99,7 +99,7 @@ human_baseline_row = {
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AutoEvalColumn.revision.name: "N/A",
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AutoEvalColumn.precision.name: None,
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AutoEvalColumn.average.name: 92.75,
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-
AutoEvalColumn.
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AutoEvalColumn.arc.name: 80.0,
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AutoEvalColumn.hellaswag.name: 95.0,
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AutoEvalColumn.mmlu.name: 89.8,
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auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
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auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
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auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
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+
auto_eval_column_dict.append(["merged", ColumnContent, ColumnContent("Merged", "bool", False)])
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auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
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auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
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auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
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AutoEvalColumn.model.name: "<p>Baseline</p>",
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AutoEvalColumn.revision.name: "N/A",
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AutoEvalColumn.precision.name: None,
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AutoEvalColumn.merged.name: False,
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AutoEvalColumn.average.name: 31.0,
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AutoEvalColumn.arc.name: 25.0,
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AutoEvalColumn.hellaswag.name: 25.0,
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AutoEvalColumn.revision.name: "N/A",
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AutoEvalColumn.precision.name: None,
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AutoEvalColumn.average.name: 92.75,
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AutoEvalColumn.merged.name: False,
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AutoEvalColumn.arc.name: 80.0,
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AutoEvalColumn.hellaswag.name: 95.0,
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AutoEvalColumn.mmlu.name: 89.8,
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src/leaderboard/read_evals.py
CHANGED
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@@ -138,7 +138,7 @@ class EvalResult:
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"eval_name": self.eval_name, # not a column, just a save name,
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AutoEvalColumn.precision.name: self.precision.value.name,
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AutoEvalColumn.model_type.name: self.model_type.value.name,
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-
AutoEvalColumn.
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AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
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AutoEvalColumn.weight_type.name: self.weight_type.value.name,
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AutoEvalColumn.architecture.name: self.architecture,
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"eval_name": self.eval_name, # not a column, just a save name,
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AutoEvalColumn.precision.name: self.precision.value.name,
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AutoEvalColumn.model_type.name: self.model_type.value.name,
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+
AutoEvalColumn.merged.name: self.merge,
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AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
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AutoEvalColumn.weight_type.name: self.weight_type.value.name,
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AutoEvalColumn.architecture.name: self.architecture,
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