Spaces:
Running
on
CPU Upgrade
Running
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()
|
|
112 |
pending_eval_queue_df,
|
113 |
) = get_evaluation_queue_df(eval_queue, eval_queue_private, EVAL_REQUESTS_PATH, EVAL_COLS)
|
114 |
|
|
|
|
|
115 |
|
116 |
## INTERACTION FUNCTIONS
|
117 |
def add_new_eval(
|
@@ -214,8 +216,8 @@ def change_tab(query_param: str):
|
|
214 |
|
215 |
|
216 |
# Searching and filtering
|
217 |
-
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):
|
218 |
-
filtered_df = filter_models(hidden_df, type_query, size_query, show_deleted)
|
219 |
if query != "":
|
220 |
filtered_df = search_table(filtered_df, query)
|
221 |
df = select_columns(filtered_df, columns)
|
@@ -247,16 +249,17 @@ NUMERIC_INTERVALS = {
|
|
247 |
}
|
248 |
|
249 |
def filter_models(
|
250 |
-
df: pd.DataFrame, type_query: list, size_query: list, show_deleted: bool
|
251 |
) -> pd.DataFrame:
|
252 |
# Show all models
|
253 |
if show_deleted:
|
254 |
filtered_df = df
|
255 |
else: # Show only still on the hub models
|
256 |
-
filtered_df = df[df[AutoEvalColumn.still_on_hub.name]
|
257 |
|
258 |
type_emoji = [t[0] for t in type_query]
|
259 |
filtered_df = filtered_df[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
|
|
|
260 |
|
261 |
numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
|
262 |
params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
|
@@ -275,6 +278,12 @@ with demo:
|
|
275 |
with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
|
276 |
with gr.Row():
|
277 |
with gr.Column():
|
|
|
|
|
|
|
|
|
|
|
|
|
278 |
with gr.Row():
|
279 |
shown_columns = gr.CheckboxGroup(
|
280 |
choices=[
|
@@ -308,11 +317,6 @@ with demo:
|
|
308 |
value=True, label="Show gated/private/deleted models", interactive=True
|
309 |
)
|
310 |
with gr.Column(min_width=320):
|
311 |
-
search_bar = gr.Textbox(
|
312 |
-
placeholder="🔍 Search for your model and press ENTER...",
|
313 |
-
show_label=False,
|
314 |
-
elem_id="search-bar",
|
315 |
-
)
|
316 |
with gr.Box(elem_id="box-filter"):
|
317 |
filter_columns_type = gr.CheckboxGroup(
|
318 |
label="Model types",
|
@@ -331,6 +335,13 @@ with demo:
|
|
331 |
interactive=True,
|
332 |
elem_id="filter-columns-type",
|
333 |
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
334 |
filter_columns_size = gr.CheckboxGroup(
|
335 |
label="Model sizes",
|
336 |
choices=list(NUMERIC_INTERVALS.keys()),
|
@@ -373,6 +384,7 @@ with demo:
|
|
373 |
leaderboard_table,
|
374 |
shown_columns,
|
375 |
filter_columns_type,
|
|
|
376 |
filter_columns_size,
|
377 |
deleted_models_visibility,
|
378 |
search_bar,
|
@@ -386,6 +398,7 @@ with demo:
|
|
386 |
leaderboard_table,
|
387 |
shown_columns,
|
388 |
filter_columns_type,
|
|
|
389 |
filter_columns_size,
|
390 |
deleted_models_visibility,
|
391 |
search_bar,
|
@@ -400,6 +413,22 @@ with demo:
|
|
400 |
leaderboard_table,
|
401 |
shown_columns,
|
402 |
filter_columns_type,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
403 |
filter_columns_size,
|
404 |
deleted_models_visibility,
|
405 |
search_bar,
|
@@ -414,6 +443,7 @@ with demo:
|
|
414 |
leaderboard_table,
|
415 |
shown_columns,
|
416 |
filter_columns_type,
|
|
|
417 |
filter_columns_size,
|
418 |
deleted_models_visibility,
|
419 |
search_bar,
|
@@ -428,6 +458,7 @@ with demo:
|
|
428 |
leaderboard_table,
|
429 |
shown_columns,
|
430 |
filter_columns_type,
|
|
|
431 |
filter_columns_size,
|
432 |
deleted_models_visibility,
|
433 |
search_bar,
|
|
|
112 |
pending_eval_queue_df,
|
113 |
) = get_evaluation_queue_df(eval_queue, eval_queue_private, EVAL_REQUESTS_PATH, EVAL_COLS)
|
114 |
|
115 |
+
print(leaderboard_df["Precision"].unique())
|
116 |
+
|
117 |
|
118 |
## INTERACTION FUNCTIONS
|
119 |
def add_new_eval(
|
|
|
216 |
|
217 |
|
218 |
# Searching and filtering
|
219 |
+
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):
|
220 |
+
filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
|
221 |
if query != "":
|
222 |
filtered_df = search_table(filtered_df, query)
|
223 |
df = select_columns(filtered_df, columns)
|
|
|
249 |
}
|
250 |
|
251 |
def filter_models(
|
252 |
+
df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
|
253 |
) -> pd.DataFrame:
|
254 |
# Show all models
|
255 |
if show_deleted:
|
256 |
filtered_df = df
|
257 |
else: # Show only still on the hub models
|
258 |
+
filtered_df = df[df[AutoEvalColumn.still_on_hub.name] is True]
|
259 |
|
260 |
type_emoji = [t[0] for t in type_query]
|
261 |
filtered_df = filtered_df[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
|
262 |
+
filtered_df = filtered_df[df[AutoEvalColumn.precision.name].isin(precision_query)]
|
263 |
|
264 |
numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
|
265 |
params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
|
|
|
278 |
with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
|
279 |
with gr.Row():
|
280 |
with gr.Column():
|
281 |
+
with gr.Row():
|
282 |
+
search_bar = gr.Textbox(
|
283 |
+
placeholder=" 🔍 Search for your model and press ENTER...",
|
284 |
+
show_label=False,
|
285 |
+
elem_id="search-bar",
|
286 |
+
)
|
287 |
with gr.Row():
|
288 |
shown_columns = gr.CheckboxGroup(
|
289 |
choices=[
|
|
|
317 |
value=True, label="Show gated/private/deleted models", interactive=True
|
318 |
)
|
319 |
with gr.Column(min_width=320):
|
|
|
|
|
|
|
|
|
|
|
320 |
with gr.Box(elem_id="box-filter"):
|
321 |
filter_columns_type = gr.CheckboxGroup(
|
322 |
label="Model types",
|
|
|
335 |
interactive=True,
|
336 |
elem_id="filter-columns-type",
|
337 |
)
|
338 |
+
filter_columns_precision = gr.CheckboxGroup(
|
339 |
+
label="Precision",
|
340 |
+
choices=["torch.float16", "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"],
|
341 |
+
value=["torch.float16", "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"],
|
342 |
+
interactive=True,
|
343 |
+
elem_id="filter-columns-precision",
|
344 |
+
)
|
345 |
filter_columns_size = gr.CheckboxGroup(
|
346 |
label="Model sizes",
|
347 |
choices=list(NUMERIC_INTERVALS.keys()),
|
|
|
384 |
leaderboard_table,
|
385 |
shown_columns,
|
386 |
filter_columns_type,
|
387 |
+
filter_columns_precision,
|
388 |
filter_columns_size,
|
389 |
deleted_models_visibility,
|
390 |
search_bar,
|
|
|
398 |
leaderboard_table,
|
399 |
shown_columns,
|
400 |
filter_columns_type,
|
401 |
+
filter_columns_precision,
|
402 |
filter_columns_size,
|
403 |
deleted_models_visibility,
|
404 |
search_bar,
|
|
|
413 |
leaderboard_table,
|
414 |
shown_columns,
|
415 |
filter_columns_type,
|
416 |
+
filter_columns_precision,
|
417 |
+
filter_columns_size,
|
418 |
+
deleted_models_visibility,
|
419 |
+
search_bar,
|
420 |
+
],
|
421 |
+
leaderboard_table,
|
422 |
+
queue=True,
|
423 |
+
)
|
424 |
+
filter_columns_precision.change(
|
425 |
+
update_table,
|
426 |
+
[
|
427 |
+
hidden_leaderboard_table_for_search,
|
428 |
+
leaderboard_table,
|
429 |
+
shown_columns,
|
430 |
+
filter_columns_type,
|
431 |
+
filter_columns_precision,
|
432 |
filter_columns_size,
|
433 |
deleted_models_visibility,
|
434 |
search_bar,
|
|
|
443 |
leaderboard_table,
|
444 |
shown_columns,
|
445 |
filter_columns_type,
|
446 |
+
filter_columns_precision,
|
447 |
filter_columns_size,
|
448 |
deleted_models_visibility,
|
449 |
search_bar,
|
|
|
458 |
leaderboard_table,
|
459 |
shown_columns,
|
460 |
filter_columns_type,
|
461 |
+
filter_columns_precision,
|
462 |
filter_columns_size,
|
463 |
deleted_models_visibility,
|
464 |
search_bar,
|