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on
CPU Upgrade
Clémentine
commited on
Commit
•
df66f6e
1
Parent(s):
bb17be3
refacto style + rate limit
Browse files- app.py +30 -22
- scripts/create_request_file.py +4 -3
- src/display/formatting.py +1 -0
- src/display/utils.py +2 -1
- src/envs.py +2 -0
- src/leaderboard/read_evals.py +9 -7
- src/populate.py +2 -2
- src/submission/check_validity.py +9 -4
- src/submission/submit.py +8 -8
- src/tools/collections.py +3 -3
- src/tools/plots.py +5 -3
app.py
CHANGED
@@ -6,18 +6,6 @@ import pandas as pd
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from apscheduler.schedulers.background import BackgroundScheduler
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from huggingface_hub import snapshot_download
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from src.display.utils import (
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COLS,
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TYPES,
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BENCHMARK_COLS,
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EVAL_COLS,
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EVAL_TYPES,
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AutoEvalColumn,
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ModelType,
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NUMERIC_INTERVALS,
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fields,
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)
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from src.display.css_html_js import custom_css, get_window_url_params
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from src.display.about import (
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CITATION_BUTTON_LABEL,
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CITATION_BUTTON_TEXT,
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@@ -26,17 +14,29 @@ from src.display.about import (
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LLM_BENCHMARKS_TEXT,
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TITLE,
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)
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from src.tools.plots import (
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create_metric_plot_obj,
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create_scores_df,
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create_plot_df,
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join_model_info_with_results,
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HUMAN_BASELINES,
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)
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from src.tools.collections import update_collections
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from src.populate import get_evaluation_queue_df, get_leaderboard_df
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from src.envs import H4_TOKEN, QUEUE_REPO, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, RESULTS_REPO, API, REPO_ID, IS_PUBLIC
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from src.submission.submit import add_new_eval
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def restart_space():
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@@ -61,9 +61,9 @@ original_df = get_leaderboard_df(EVAL_RESULTS_PATH, COLS, BENCHMARK_COLS)
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update_collections(original_df.copy())
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leaderboard_df = original_df.copy()
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-
#models = original_df["model_name_for_query"].tolist() # needed for model backlinks in their to the leaderboard
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# plot_df = create_plot_df(create_scores_df(join_model_info_with_results(original_df)))
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-
#to_be_dumped = f"models = {repr(models)}\n"
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(
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finished_eval_queue_df,
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@@ -173,8 +173,16 @@ with demo:
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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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-
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label="Select columns to show",
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elem_id="column-select",
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interactive=True,
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from apscheduler.schedulers.background import BackgroundScheduler
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from huggingface_hub import snapshot_download
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from src.display.about import (
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CITATION_BUTTON_LABEL,
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CITATION_BUTTON_TEXT,
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LLM_BENCHMARKS_TEXT,
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TITLE,
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)
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+
from src.display.css_html_js import custom_css, get_window_url_params
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+
from src.display.utils import (
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BENCHMARK_COLS,
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COLS,
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+
EVAL_COLS,
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+
EVAL_TYPES,
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+
NUMERIC_INTERVALS,
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+
TYPES,
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AutoEvalColumn,
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ModelType,
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fields,
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)
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from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO
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from src.populate import get_evaluation_queue_df, get_leaderboard_df
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from src.submission.submit import add_new_eval
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from src.tools.collections import update_collections
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from src.tools.plots import (
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HUMAN_BASELINES,
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create_metric_plot_obj,
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create_plot_df,
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create_scores_df,
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join_model_info_with_results,
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)
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def restart_space():
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update_collections(original_df.copy())
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leaderboard_df = original_df.copy()
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+
# models = original_df["model_name_for_query"].tolist() # needed for model backlinks in their to the leaderboard
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# plot_df = create_plot_df(create_scores_df(join_model_info_with_results(original_df)))
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# to_be_dumped = f"models = {repr(models)}\n"
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(
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finished_eval_queue_df,
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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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c.name
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for c in fields(AutoEvalColumn)
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if not c.hidden and not c.never_hidden and not c.dummy
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],
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value=[
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c.name
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for c in fields(AutoEvalColumn)
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if c.displayed_by_default and not c.hidden and not c.never_hidden
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],
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label="Select columns to show",
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elem_id="column-select",
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interactive=True,
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scripts/create_request_file.py
CHANGED
@@ -1,11 +1,12 @@
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from datetime import datetime, timezone
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import json
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import os
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import re
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import click
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from huggingface_hub import HfApi, snapshot_download
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from colorama import Fore
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import
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EVAL_REQUESTS_PATH = "eval-queue"
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QUEUE_REPO = "open-llm-leaderboard/requests"
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import json
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import os
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import pprint
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import re
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from datetime import datetime, timezone
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+
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import click
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from colorama import Fore
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from huggingface_hub import HfApi, snapshot_download
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EVAL_REQUESTS_PATH = "eval-queue"
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QUEUE_REPO = "open-llm-leaderboard/requests"
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src/display/formatting.py
CHANGED
@@ -1,4 +1,5 @@
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import os
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from huggingface_hub import HfApi
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API = HfApi()
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import os
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from huggingface_hub import HfApi
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API = HfApi()
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src/display/utils.py
CHANGED
@@ -1,7 +1,8 @@
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from dataclasses import dataclass
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import pandas as pd
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from enum import Enum
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# These classes are for user facing column names,
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# to avoid having to change them all around the code
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from dataclasses import dataclass
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from enum import Enum
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import pandas as pd
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# These classes are for user facing column names,
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# to avoid having to change them all around the code
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src/envs.py
CHANGED
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import os
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from huggingface_hub import HfApi
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# clone / pull the lmeh eval data
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@@ -24,5 +25,6 @@ PATH_TO_COLLECTION = "open-llm-leaderboard/llm-leaderboard-best-models-652d6c796
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# Rate limit variables
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RATE_LIMIT_PERIOD = 7
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RATE_LIMIT_QUOTA = 5
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API = HfApi(token=H4_TOKEN)
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import os
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from huggingface_hub import HfApi
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# clone / pull the lmeh eval data
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# Rate limit variables
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RATE_LIMIT_PERIOD = 7
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RATE_LIMIT_QUOTA = 5
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HAS_HIGHER_RATE_LIMIT = ["TheBloke"]
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API = HfApi(token=H4_TOKEN)
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src/leaderboard/read_evals.py
CHANGED
@@ -1,15 +1,15 @@
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import json
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-
import os
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import math
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import
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from dataclasses import dataclass
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from typing import Dict, List, Tuple
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import dateutil
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import numpy as np
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from src.display.utils import AutoEvalColumn, ModelType, Tasks
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from src.display.formatting import make_clickable_model
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from src.submission.check_validity import is_model_on_hub
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@@ -56,7 +56,9 @@ class EvalResult:
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model = org_and_model[1]
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result_key = f"{org}_{model}_{precision}"
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still_on_hub = is_model_on_hub(
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# Extract results available in this file (some results are split in several files)
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results = {}
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continue
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# Some truthfulQA values are NaNs
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if task.benchmark == "truthfulqa:mc" and
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if math.isnan(float(data["results"][
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results[task.benchmark] = 0.0
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continue
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for v in eval_results.values():
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try:
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results.append(v.to_dict())
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except KeyError:
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continue
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return results
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import glob
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import json
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import math
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import os
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from dataclasses import dataclass
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from typing import Dict, List, Tuple
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import dateutil
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import numpy as np
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from src.display.formatting import make_clickable_model
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from src.display.utils import AutoEvalColumn, ModelType, Tasks
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from src.submission.check_validity import is_model_on_hub
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model = org_and_model[1]
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result_key = f"{org}_{model}_{precision}"
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still_on_hub = is_model_on_hub(
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"/".join(org_and_model), config.get("model_sha", "main"), trust_remote_code=True
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)[0]
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# Extract results available in this file (some results are split in several files)
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results = {}
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continue
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# Some truthfulQA values are NaNs
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if task.benchmark == "truthfulqa:mc" and "harness|truthfulqa:mc|0" in data["results"]:
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if math.isnan(float(data["results"]["harness|truthfulqa:mc|0"][task.metric])):
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results[task.benchmark] = 0.0
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continue
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for v in eval_results.values():
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try:
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results.append(v.to_dict())
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except KeyError: # not all eval values present
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continue
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return results
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src/populate.py
CHANGED
@@ -3,10 +3,10 @@ import os
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import pandas as pd
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from src.leaderboard.filter_models import filter_models
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from src.leaderboard.read_evals import get_eval_results
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from src.display.formatting import make_clickable_model, has_no_nan_values
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from src.display.utils import AutoEvalColumn, EvalQueueColumn, baseline_row
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def get_leaderboard_df(results_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
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import pandas as pd
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from src.display.formatting import has_no_nan_values, make_clickable_model
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from src.display.utils import AutoEvalColumn, EvalQueueColumn, baseline_row
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from src.leaderboard.filter_models import filter_models
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from src.leaderboard.read_evals import get_eval_results
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def get_leaderboard_df(results_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
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src/submission/check_validity.py
CHANGED
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import huggingface_hub
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import os
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import json
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import re
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from collections import defaultdict
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from
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from huggingface_hub import ModelCard
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from transformers import AutoConfig
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from
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# ht to @Wauplin, thank you for the snippet!
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submissions_after_timelimit = [d for d in submission_dates if d > time_limit]
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num_models_submitted_in_period = len(submissions_after_timelimit)
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if num_models_submitted_in_period > rate_limit_quota:
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error_msg = f"Organisation or user `{org_or_user}`"
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error_msg += f"already has {num_models_submitted_in_period} model requests submitted to the leaderboard "
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import json
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import os
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import re
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from collections import defaultdict
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from datetime import datetime, timedelta, timezone
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+
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import huggingface_hub
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from huggingface_hub import ModelCard
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from huggingface_hub.hf_api import ModelInfo
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from transformers import AutoConfig
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from src.envs import HAS_HIGHER_RATE_LIMIT
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# ht to @Wauplin, thank you for the snippet!
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submissions_after_timelimit = [d for d in submission_dates if d > time_limit]
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num_models_submitted_in_period = len(submissions_after_timelimit)
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if org_or_user in HAS_HIGHER_RATE_LIMIT:
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rate_limit_quota = 2 * rate_limit_quota
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if num_models_submitted_in_period > rate_limit_quota:
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error_msg = f"Organisation or user `{org_or_user}`"
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error_msg += f"already has {num_models_submitted_in_period} model requests submitted to the leaderboard "
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src/submission/submit.py
CHANGED
@@ -1,17 +1,17 @@
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-
import
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from datetime import datetime, timezone
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from src.display.formatting import styled_error,
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from src.leaderboard.filter_models import DO_NOT_SUBMIT_MODELS
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from src.submission.check_validity import (
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user_submission_permission,
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is_model_on_hub,
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get_model_size,
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check_model_card,
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already_submitted_models,
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)
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from src.envs import RATE_LIMIT_QUOTA, RATE_LIMIT_PERIOD, H4_TOKEN, EVAL_REQUESTS_PATH, API, QUEUE_REPO
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requested_models, users_to_submission_dates = already_submitted_models(EVAL_REQUESTS_PATH)
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import json
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import os
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from datetime import datetime, timezone
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+
from src.display.formatting import styled_error, styled_message, styled_warning
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+
from src.envs import API, EVAL_REQUESTS_PATH, H4_TOKEN, QUEUE_REPO, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA
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from src.leaderboard.filter_models import DO_NOT_SUBMIT_MODELS
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from src.submission.check_validity import (
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9 |
already_submitted_models,
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check_model_card,
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get_model_size,
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12 |
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is_model_on_hub,
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13 |
+
user_submission_permission,
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)
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requested_models, users_to_submission_dates = already_submitted_models(EVAL_REQUESTS_PATH)
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src/tools/collections.py
CHANGED
@@ -1,11 +1,11 @@
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import os
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import pandas as pd
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-
from
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from huggingface_hub import get_collection, add_collection_item, update_collection_item, delete_collection_item
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from huggingface_hub.utils._errors import HfHubHTTPError
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6 |
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from src.display.utils import AutoEvalColumn, ModelType
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8 |
-
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from src.envs import H4_TOKEN, PATH_TO_COLLECTION
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# Specific intervals for the collections
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import os
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+
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import pandas as pd
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+
from huggingface_hub import add_collection_item, delete_collection_item, get_collection, update_collection_item
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5 |
from huggingface_hub.utils._errors import HfHubHTTPError
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+
from pandas import DataFrame
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from src.display.utils import AutoEvalColumn, ModelType
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9 |
from src.envs import H4_TOKEN, PATH_TO_COLLECTION
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10 |
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# Specific intervals for the collections
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src/tools/plots.py
CHANGED
@@ -1,9 +1,11 @@
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1 |
import pandas as pd
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2 |
import plotly.express as px
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3 |
from plotly.graph_objs import Figure
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4 |
-
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5 |
-
from datetime import datetime, timezone
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6 |
-
from typing import List, Dict, Tuple, Any
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7 |
from src.leaderboard.filter_models import FLAGGED_MODELS
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8 |
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9 |
# Average ⬆️ human baseline is 0.897 (source: averaging human baselines below)
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|
1 |
+
import pickle
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2 |
+
from datetime import datetime, timezone
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3 |
+
from typing import Any, Dict, List, Tuple
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4 |
+
|
5 |
import pandas as pd
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6 |
import plotly.express as px
|
7 |
from plotly.graph_objs import Figure
|
8 |
+
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|
|
|
|
9 |
from src.leaderboard.filter_models import FLAGGED_MODELS
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10 |
|
11 |
# Average ⬆️ human baseline is 0.897 (source: averaging human baselines below)
|