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from time import sleep |
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import gradio as gr |
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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 gradio_space_ci import enable_space_ci |
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from src.display.about import ( |
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INTRODUCTION_TEXT, |
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LLM_BENCHMARKS_TEXT, |
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CITATION_BUTTON_LABEL, |
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CITATION_BUTTON_TEXT, |
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TITLE, |
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) |
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from src.display.css_html_js import custom_css |
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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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WeightType, |
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Precision, |
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) |
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from src.envs import API, EVAL_RESULTS_PATH, RESULTS_REPO, REPO_ID, HF_TOKEN |
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from src.populate import get_leaderboard_df |
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from src.tools.plots import ( |
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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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) |
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def restart_space(): |
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API.restart_space(repo_id=REPO_ID, token=HF_TOKEN) |
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def init_space(): |
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try: |
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print(EVAL_RESULTS_PATH) |
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snapshot_download( |
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repo_id=RESULTS_REPO, |
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local_dir=EVAL_RESULTS_PATH, |
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repo_type="dataset", |
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tqdm_class=None, |
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etag_timeout=30, |
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resume_download=True, |
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) |
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except Exception as e: |
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print(e) |
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sleep(180) |
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return init_space() |
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raw_data, original_df = get_leaderboard_df( |
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results_path=EVAL_RESULTS_PATH, cols=COLS, benchmark_cols=BENCHMARK_COLS |
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) |
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leaderboard_df = original_df.copy() |
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plot_df = create_plot_df(create_scores_df(raw_data)) |
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return leaderboard_df, original_df, plot_df |
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leaderboard_df, original_df, plot_df = init_space() |
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def update_table( |
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hidden_df: pd.DataFrame, |
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columns: list, |
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type_query: list, |
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weight_precision_query: str, |
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activation_precision_query: str, |
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size_query: list, |
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hide_models: list, |
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query: str, |
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): |
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filtered_df = filter_models( |
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df=hidden_df, |
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type_query=type_query, |
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size_query=size_query, |
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weight_precision_query=weight_precision_query, |
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activation_precision_query=activation_precision_query, |
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hide_models=hide_models, |
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) |
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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 load_query(request: gr.Request): |
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query = request.query_params.get("query") or "" |
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return ( |
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query, |
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query, |
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) |
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def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: |
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return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))] |
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def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame: |
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always_here_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden] |
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dummy_col = [AutoEvalColumn.dummy.name] |
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filtered_df = df[always_here_cols + [c for c in COLS if c in df.columns and c in columns] + dummy_col] |
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return filtered_df |
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def filter_queries(query: str, filtered_df: pd.DataFrame): |
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"""Added by Abishek""" |
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final_df = [] |
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if query != "": |
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queries = [q.strip() for q in query.split(";")] |
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for _q in queries: |
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_q = _q.strip() |
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if _q != "": |
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temp_filtered_df = search_table(filtered_df, _q) |
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if len(temp_filtered_df) > 0: |
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final_df.append(temp_filtered_df) |
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if len(final_df) > 0: |
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filtered_df = pd.concat(final_df) |
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filtered_df = filtered_df.drop_duplicates( |
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subset=[ |
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AutoEvalColumn.model.name, |
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AutoEvalColumn.weight_precision.name, |
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AutoEvalColumn.activation_precision.name, |
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AutoEvalColumn.revision.name, |
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] |
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) |
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return filtered_df |
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def filter_models( |
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df: pd.DataFrame, |
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type_query: list, |
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size_query: list, |
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weight_precision_query: list, |
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activation_precision_query: list, |
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hide_models: list, |
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) -> pd.DataFrame: |
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if "Private or deleted" in hide_models: |
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filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True] |
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else: |
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filtered_df = df |
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if "Contains a merge/moerge" in hide_models: |
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filtered_df = filtered_df[filtered_df[AutoEvalColumn.merged.name] == False] |
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if "MoE" in hide_models: |
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filtered_df = filtered_df[filtered_df[AutoEvalColumn.moe.name] == False] |
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if "Flagged" in hide_models: |
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filtered_df = filtered_df[filtered_df[AutoEvalColumn.flagged.name] == False] |
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type_emoji = [t[0] for t in type_query] |
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filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)] |
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filtered_df = filtered_df.loc[df[AutoEvalColumn.weight_precision.name].isin(weight_precision_query + ["None"])] |
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filtered_df = filtered_df.loc[ |
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df[AutoEvalColumn.activation_precision.name].isin(activation_precision_query + ["None"]) |
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] |
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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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mask = params_column.apply(lambda x: any(numeric_interval.contains(x))) |
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filtered_df = filtered_df.loc[mask] |
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return filtered_df |
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leaderboard_df = filter_models( |
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df=leaderboard_df, |
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type_query=[t.to_str(" : ") for t in ModelType], |
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size_query=list(NUMERIC_INTERVALS.keys()), |
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weight_precision_query=[i.value.name for i in Precision], |
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activation_precision_query=[i.value.name for i in Precision], |
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hide_models=["Private or deleted", "Contains a merge/moerge", "Flagged"], |
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) |
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demo = gr.Blocks(css=custom_css) |
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with demo: |
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gr.HTML(TITLE) |
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gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") |
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with gr.Tabs(elem_classes="tab-buttons") as tabs: |
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with gr.TabItem("π
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=" π Search for your model (separate multiple queries with `;`) 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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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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) |
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with gr.Row(): |
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hide_models = gr.CheckboxGroup( |
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label="Hide models", |
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choices=["Private or deleted", "Contains a merge/moerge", "Flagged", "MoE"], |
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value=["Private or deleted", "Contains a merge/moerge", "Flagged"], |
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interactive=True, |
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) |
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with gr.Column(min_width=320): |
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filter_columns_type = gr.CheckboxGroup( |
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label="Model types", |
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choices=[t.to_str() for t in ModelType], |
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value=[t.to_str() for t in ModelType], |
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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_weight_precision = gr.CheckboxGroup( |
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label="Weight Precision", |
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choices=[i.value.name for i in Precision], |
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value=[i.value.name for i in Precision], |
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interactive=True, |
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elem_id="filter-columns-weight-precision", |
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) |
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filter_columns_activation_precision = gr.CheckboxGroup( |
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label="Activation Precision", |
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choices=[i.value.name for i in Precision], |
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value=[i.value.name for i in Precision], |
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interactive=True, |
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elem_id="filter-columns-activation-precision", |
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) |
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filter_columns_size = gr.CheckboxGroup( |
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label="Model sizes (in billions of parameters)", |
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choices=list(NUMERIC_INTERVALS.keys()), |
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value=list(NUMERIC_INTERVALS.keys()), |
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interactive=True, |
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elem_id="filter-columns-size", |
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) |
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leaderboard_table = gr.components.Dataframe( |
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value=leaderboard_df[ |
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[c.name for c in fields(AutoEvalColumn) if c.never_hidden] |
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+ shown_columns.value |
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+ [AutoEvalColumn.dummy.name] |
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], |
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headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value, |
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datatype=TYPES, |
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elem_id="leaderboard-table", |
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interactive=False, |
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visible=True, |
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) |
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hidden_leaderboard_table_for_search = gr.components.Dataframe( |
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value=original_df[COLS], |
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headers=COLS, |
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datatype=TYPES, |
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visible=False, |
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) |
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search_bar.submit( |
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update_table, |
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[ |
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hidden_leaderboard_table_for_search, |
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shown_columns, |
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filter_columns_type, |
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filter_columns_weight_precision, |
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filter_columns_activation_precision, |
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filter_columns_size, |
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hide_models, |
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search_bar, |
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], |
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leaderboard_table, |
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) |
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hidden_search_bar = gr.Textbox(value="", visible=False) |
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hidden_search_bar.change( |
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update_table, |
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[ |
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hidden_leaderboard_table_for_search, |
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shown_columns, |
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filter_columns_type, |
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filter_columns_weight_precision, |
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filter_columns_activation_precision, |
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filter_columns_size, |
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hide_models, |
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search_bar, |
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], |
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leaderboard_table, |
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) |
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demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar]) |
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for selector in [ |
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shown_columns, |
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filter_columns_type, |
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filter_columns_weight_precision, |
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filter_columns_activation_precision, |
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filter_columns_size, |
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hide_models, |
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]: |
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selector.change( |
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update_table, |
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[ |
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hidden_leaderboard_table_for_search, |
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shown_columns, |
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filter_columns_type, |
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filter_columns_weight_precision, |
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filter_columns_activation_precision, |
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filter_columns_size, |
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hide_models, |
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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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with gr.TabItem("π Metrics through time", elem_id="llm-benchmark-tab-table", id=4): |
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with gr.Row(): |
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with gr.Column(): |
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chart = create_metric_plot_obj( |
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plot_df, |
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[AutoEvalColumn.average.name], |
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title="Average of Top Scores and Human Baseline Over Time (from last update)", |
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) |
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gr.Plot(value=chart, min_width=500) |
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with gr.Column(): |
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chart = create_metric_plot_obj( |
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plot_df, |
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BENCHMARK_COLS, |
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title="Top Scores and Human Baseline Over Time (from last update)", |
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) |
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gr.Plot(value=chart, min_width=500) |
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with gr.TabItem("π About", elem_id="llm-benchmark-tab-table", id=2): |
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gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") |
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with gr.Row(): |
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with gr.Accordion("π Citation", open=False): |
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citation_button = gr.Textbox( |
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value=CITATION_BUTTON_TEXT, |
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label=CITATION_BUTTON_LABEL, |
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lines=20, |
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elem_id="citation-button", |
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show_copy_button=True, |
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) |
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scheduler = BackgroundScheduler() |
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scheduler.add_job(restart_space, "interval", seconds=1800) |
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scheduler.start() |
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demo.queue(default_concurrency_limit=40).launch() |
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