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| import json | |
| import os | |
| from datetime import datetime, timezone | |
| import gradio as gr | |
| import pandas as pd | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| from huggingface_hub import HfApi, snapshot_download | |
| from src.assets.css_html_js import custom_css, get_window_url_params | |
| from src.assets.text_content import ( | |
| CITATION_BUTTON_LABEL, | |
| CITATION_BUTTON_TEXT, | |
| EVALUATION_QUEUE_TEXT, | |
| INTRODUCTION_TEXT, | |
| LLM_BENCHMARKS_TEXT, | |
| TITLE, | |
| ) | |
| from src.plots.plot_results import ( | |
| create_metric_plot_obj, | |
| create_scores_df, | |
| create_plot_df, | |
| join_model_info_with_results, | |
| HUMAN_BASELINES, | |
| ) | |
| from src.get_model_info.apply_metadata_to_df import DO_NOT_SUBMIT_MODELS, ModelType | |
| from src.get_model_info.get_metadata_from_hub import get_model_size | |
| from src.filters import check_model_card | |
| from src.get_model_info.utils import ( | |
| AutoEvalColumn, | |
| EvalQueueColumn, | |
| fields, | |
| styled_error, | |
| styled_message, | |
| styled_warning, | |
| ) | |
| from src.manage_collections import update_collections | |
| from src.load_from_hub import get_all_requested_models, get_evaluation_queue_df, get_leaderboard_df | |
| from src.filters import is_model_on_hub, user_submission_permission | |
| pd.set_option("display.precision", 1) | |
| # clone / pull the lmeh eval data | |
| H4_TOKEN = os.environ.get("H4_TOKEN", None) | |
| QUEUE_REPO = "open-llm-leaderboard/requests" | |
| RESULTS_REPO = "open-llm-leaderboard/results" | |
| PRIVATE_QUEUE_REPO = "open-llm-leaderboard/private-requests" | |
| PRIVATE_RESULTS_REPO = "open-llm-leaderboard/private-results" | |
| IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True)) | |
| EVAL_REQUESTS_PATH = "eval-queue" | |
| EVAL_RESULTS_PATH = "eval-results" | |
| EVAL_REQUESTS_PATH_PRIVATE = "eval-queue-private" | |
| EVAL_RESULTS_PATH_PRIVATE = "eval-results-private" | |
| api = HfApi(token=H4_TOKEN) | |
| def restart_space(): | |
| api.restart_space(repo_id="HuggingFaceH4/open_llm_leaderboard", token=H4_TOKEN) | |
| # Rate limit variables | |
| RATE_LIMIT_PERIOD = 7 | |
| RATE_LIMIT_QUOTA = 5 | |
| # Column selection | |
| COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden] | |
| TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden] | |
| COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden] | |
| TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden] | |
| if not IS_PUBLIC: | |
| COLS.insert(2, AutoEvalColumn.precision.name) | |
| TYPES.insert(2, AutoEvalColumn.precision.type) | |
| EVAL_COLS = [c.name for c in fields(EvalQueueColumn)] | |
| EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)] | |
| BENCHMARK_COLS = [ | |
| c.name | |
| for c in [ | |
| AutoEvalColumn.arc, | |
| AutoEvalColumn.hellaswag, | |
| AutoEvalColumn.mmlu, | |
| AutoEvalColumn.truthfulqa, | |
| AutoEvalColumn.winogrande, | |
| AutoEvalColumn.gsm8k, | |
| AutoEvalColumn.drop | |
| ] | |
| ] | |
| try: | |
| snapshot_download(repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30) | |
| except Exception: | |
| restart_space() | |
| try: | |
| snapshot_download(repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30) | |
| except Exception: | |
| restart_space() | |
| requested_models, users_to_submission_dates = get_all_requested_models(EVAL_REQUESTS_PATH) | |
| original_df = get_leaderboard_df(EVAL_RESULTS_PATH, COLS, BENCHMARK_COLS) | |
| update_collections(original_df.copy()) | |
| leaderboard_df = original_df.copy() | |
| models = original_df["model_name_for_query"].tolist() # needed for model backlinks in their to the leaderboard | |
| #plot_df = create_plot_df(create_scores_df(join_model_info_with_results(original_df))) | |
| to_be_dumped = f"models = {repr(models)}\n" | |
| ( | |
| finished_eval_queue_df, | |
| running_eval_queue_df, | |
| pending_eval_queue_df, | |
| ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) | |
| ## INTERACTION FUNCTIONS | |
| def add_new_eval( | |
| model: str, | |
| base_model: str, | |
| revision: str, | |
| precision: str, | |
| private: bool, | |
| weight_type: str, | |
| model_type: str, | |
| ): | |
| precision = precision.split(" ")[0] | |
| current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") | |
| if model_type is None or model_type == "": | |
| return styled_error("Please select a model type.") | |
| # Is the user rate limited? | |
| user_can_submit, error_msg = user_submission_permission(model, users_to_submission_dates, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA) | |
| if not user_can_submit: | |
| return styled_error(error_msg) | |
| # Did the model authors forbid its submission to the leaderboard? | |
| if model in DO_NOT_SUBMIT_MODELS or base_model in DO_NOT_SUBMIT_MODELS: | |
| return styled_warning("Model authors have requested that their model be not submitted on the leaderboard.") | |
| # Does the model actually exist? | |
| if revision == "": | |
| revision = "main" | |
| if weight_type in ["Delta", "Adapter"]: | |
| base_model_on_hub, error = is_model_on_hub(base_model, revision, H4_TOKEN) | |
| if not base_model_on_hub: | |
| return styled_error(f'Base model "{base_model}" {error}') | |
| if not weight_type == "Adapter": | |
| model_on_hub, error = is_model_on_hub(model, revision) | |
| if not model_on_hub: | |
| return styled_error(f'Model "{model}" {error}') | |
| try: | |
| model_info = api.model_info(repo_id=model, revision=revision) | |
| except Exception: | |
| return styled_error("Could not get your model information. Please fill it up properly.") | |
| model_size = get_model_size(model_info=model_info , precision= precision) | |
| # Were the model card and license filled? | |
| try: | |
| license = model_info.cardData["license"] | |
| except Exception: | |
| return styled_error("Please select a license for your model") | |
| modelcard_OK, error_msg = check_model_card(model) | |
| if not modelcard_OK: | |
| return styled_error(error_msg) | |
| # Seems good, creating the eval | |
| print("Adding new eval") | |
| eval_entry = { | |
| "model": model, | |
| "base_model": base_model, | |
| "revision": revision, | |
| "private": private, | |
| "precision": precision, | |
| "weight_type": weight_type, | |
| "status": "PENDING", | |
| "submitted_time": current_time, | |
| "model_type": model_type, | |
| "likes": model_info.likes, | |
| "params": model_size, | |
| "license": license, | |
| } | |
| user_name = "" | |
| model_path = model | |
| if "/" in model: | |
| user_name = model.split("/")[0] | |
| model_path = model.split("/")[1] | |
| print("Creating eval file") | |
| OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}" | |
| os.makedirs(OUT_DIR, exist_ok=True) | |
| out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{precision}_{weight_type}.json" | |
| # Check for duplicate submission | |
| if f"{model}_{revision}_{precision}" in requested_models: | |
| return styled_warning("This model has been already submitted.") | |
| with open(out_path, "w") as f: | |
| f.write(json.dumps(eval_entry)) | |
| print("Uploading eval file") | |
| api.upload_file( | |
| path_or_fileobj=out_path, | |
| path_in_repo=out_path.split("eval-queue/")[1], | |
| repo_id=QUEUE_REPO, | |
| repo_type="dataset", | |
| commit_message=f"Add {model} to eval queue", | |
| ) | |
| # Remove the local file | |
| os.remove(out_path) | |
| return styled_message( | |
| "Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list." | |
| ) | |
| # Basics | |
| def change_tab(query_param: str): | |
| query_param = query_param.replace("'", '"') | |
| query_param = json.loads(query_param) | |
| if isinstance(query_param, dict) and "tab" in query_param and query_param["tab"] == "evaluation": | |
| return gr.Tabs.update(selected=1) | |
| else: | |
| return gr.Tabs.update(selected=0) | |
| # Searching and filtering | |
| def update_table( | |
| hidden_df: pd.DataFrame, | |
| columns: list, | |
| type_query: list, | |
| precision_query: str, | |
| size_query: list, | |
| show_deleted: bool, | |
| query: str, | |
| ): | |
| filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted) | |
| filtered_df = filter_queries(query, filtered_df) | |
| df = select_columns(filtered_df, columns) | |
| return df | |
| def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: | |
| return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))] | |
| def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame: | |
| always_here_cols = [ | |
| AutoEvalColumn.model_type_symbol.name, | |
| AutoEvalColumn.model.name, | |
| ] | |
| # We use COLS to maintain sorting | |
| filtered_df = df[ | |
| always_here_cols + [c for c in COLS if c in df.columns and c in columns] + [AutoEvalColumn.dummy.name] | |
| ] | |
| return filtered_df | |
| NUMERIC_INTERVALS = { | |
| "?": pd.Interval(-1, 0, closed="right"), | |
| "~1.5": pd.Interval(0, 2, closed="right"), | |
| "~3": pd.Interval(2, 4, closed="right"), | |
| "~7": pd.Interval(4, 9, closed="right"), | |
| "~13": pd.Interval(9, 20, closed="right"), | |
| "~35": pd.Interval(20, 45, closed="right"), | |
| "~60": pd.Interval(45, 70, closed="right"), | |
| "70+": pd.Interval(70, 10000, closed="right"), | |
| } | |
| def filter_queries(query: str, filtered_df: pd.DataFrame): | |
| """Added by Abishek""" | |
| final_df = [] | |
| if query != "": | |
| queries = [q.strip() for q in query.split(";")] | |
| for _q in queries: | |
| _q = _q.strip() | |
| if _q != "": | |
| temp_filtered_df = search_table(filtered_df, _q) | |
| if len(temp_filtered_df) > 0: | |
| final_df.append(temp_filtered_df) | |
| if len(final_df) > 0: | |
| filtered_df = pd.concat(final_df) | |
| filtered_df = filtered_df.drop_duplicates( | |
| subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name] | |
| ) | |
| return filtered_df | |
| def filter_models( | |
| df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool | |
| ) -> pd.DataFrame: | |
| # Show all models | |
| if show_deleted: | |
| filtered_df = df | |
| else: # Show only still on the hub models | |
| filtered_df = df[df[AutoEvalColumn.still_on_hub.name] is True] | |
| type_emoji = [t[0] for t in type_query] | |
| filtered_df = filtered_df[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)] | |
| filtered_df = filtered_df[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])] | |
| numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query])) | |
| params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce") | |
| mask = params_column.apply(lambda x: any(numeric_interval.contains(x))) | |
| filtered_df = filtered_df.loc[mask] | |
| return filtered_df | |
| demo = gr.Blocks(css=custom_css) | |
| with demo: | |
| gr.HTML(TITLE) | |
| gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") | |
| with gr.Tabs(elem_classes="tab-buttons") as tabs: | |
| with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| search_bar = gr.Textbox( | |
| placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...", | |
| show_label=False, | |
| elem_id="search-bar", | |
| ) | |
| with gr.Row(): | |
| shown_columns = gr.CheckboxGroup( | |
| choices=[ | |
| c | |
| for c in COLS | |
| if c | |
| not in [ | |
| AutoEvalColumn.dummy.name, | |
| AutoEvalColumn.model.name, | |
| AutoEvalColumn.model_type_symbol.name, | |
| AutoEvalColumn.still_on_hub.name, | |
| ] | |
| ], | |
| value=[ | |
| c | |
| for c in COLS_LITE | |
| if c | |
| not in [ | |
| AutoEvalColumn.dummy.name, | |
| AutoEvalColumn.model.name, | |
| AutoEvalColumn.model_type_symbol.name, | |
| AutoEvalColumn.still_on_hub.name, | |
| ] | |
| ], | |
| label="Select columns to show", | |
| elem_id="column-select", | |
| interactive=True, | |
| ) | |
| with gr.Row(): | |
| deleted_models_visibility = gr.Checkbox( | |
| value=True, label="Show gated/private/deleted models", interactive=True | |
| ) | |
| with gr.Column(min_width=320): | |
| with gr.Box(elem_id="box-filter"): | |
| filter_columns_type = gr.CheckboxGroup( | |
| label="Model types", | |
| choices=[ | |
| ModelType.PT.to_str(), | |
| ModelType.FT.to_str(), | |
| ModelType.IFT.to_str(), | |
| ModelType.RL.to_str(), | |
| ModelType.Unknown.to_str(), | |
| ], | |
| value=[ | |
| ModelType.PT.to_str(), | |
| ModelType.FT.to_str(), | |
| ModelType.IFT.to_str(), | |
| ModelType.RL.to_str(), | |
| ModelType.Unknown.to_str(), | |
| ], | |
| interactive=True, | |
| elem_id="filter-columns-type", | |
| ) | |
| filter_columns_precision = gr.CheckboxGroup( | |
| label="Precision", | |
| choices=["torch.float16", "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"], | |
| value=["torch.float16", "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"], | |
| interactive=True, | |
| elem_id="filter-columns-precision", | |
| ) | |
| filter_columns_size = gr.CheckboxGroup( | |
| label="Model sizes (in billions of parameters)", | |
| choices=list(NUMERIC_INTERVALS.keys()), | |
| value=list(NUMERIC_INTERVALS.keys()), | |
| interactive=True, | |
| elem_id="filter-columns-size", | |
| ) | |
| leaderboard_table = gr.components.Dataframe( | |
| value=leaderboard_df[ | |
| [AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name] | |
| + shown_columns.value | |
| + [AutoEvalColumn.dummy.name] | |
| ], | |
| headers=[ | |
| AutoEvalColumn.model_type_symbol.name, | |
| AutoEvalColumn.model.name, | |
| ] | |
| + shown_columns.value | |
| + [AutoEvalColumn.dummy.name], | |
| datatype=TYPES, | |
| max_rows=None, | |
| elem_id="leaderboard-table", | |
| interactive=False, | |
| visible=True, | |
| ) | |
| # Dummy leaderboard for handling the case when the user uses backspace key | |
| hidden_leaderboard_table_for_search = gr.components.Dataframe( | |
| value=original_df, | |
| headers=COLS, | |
| datatype=TYPES, | |
| max_rows=None, | |
| visible=False, | |
| ) | |
| search_bar.submit( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| filter_columns_type, | |
| filter_columns_precision, | |
| filter_columns_size, | |
| deleted_models_visibility, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| ) | |
| shown_columns.change( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| filter_columns_type, | |
| filter_columns_precision, | |
| filter_columns_size, | |
| deleted_models_visibility, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| queue=True, | |
| ) | |
| filter_columns_type.change( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| filter_columns_type, | |
| filter_columns_precision, | |
| filter_columns_size, | |
| deleted_models_visibility, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| queue=True, | |
| ) | |
| filter_columns_precision.change( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| filter_columns_type, | |
| filter_columns_precision, | |
| filter_columns_size, | |
| deleted_models_visibility, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| queue=True, | |
| ) | |
| filter_columns_size.change( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| filter_columns_type, | |
| filter_columns_precision, | |
| filter_columns_size, | |
| deleted_models_visibility, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| queue=True, | |
| ) | |
| deleted_models_visibility.change( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| filter_columns_type, | |
| filter_columns_precision, | |
| filter_columns_size, | |
| deleted_models_visibility, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| queue=True, | |
| ) | |
| # with gr.TabItem("📈 Metrics evolution through time", elem_id="llm-benchmark-tab-table", id=4): | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # chart = create_metric_plot_obj( | |
| # plot_df, | |
| # ["Average ⬆️"], | |
| # HUMAN_BASELINES, | |
| # title="Average of Top Scores and Human Baseline Over Time", | |
| # ) | |
| # gr.Plot(value=chart, interactive=False, width=500, height=500) | |
| # with gr.Column(): | |
| # chart = create_metric_plot_obj( | |
| # plot_df, | |
| # ["ARC", "HellaSwag", "MMLU", "TruthfulQA", "Winogrande", "GSM8K", "DROP"], | |
| # HUMAN_BASELINES, | |
| # title="Top Scores and Human Baseline Over Time", | |
| # ) | |
| # gr.Plot(value=chart, interactive=False, width=500, height=500) | |
| with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2): | |
| gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") | |
| with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3): | |
| with gr.Column(): | |
| with gr.Row(): | |
| gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") | |
| with gr.Column(): | |
| with gr.Accordion( | |
| f"✅ Finished Evaluations ({len(finished_eval_queue_df)})", | |
| open=False, | |
| ): | |
| with gr.Row(): | |
| finished_eval_table = gr.components.Dataframe( | |
| value=finished_eval_queue_df, | |
| headers=EVAL_COLS, | |
| datatype=EVAL_TYPES, | |
| max_rows=5, | |
| ) | |
| with gr.Accordion( | |
| f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})", | |
| open=False, | |
| ): | |
| with gr.Row(): | |
| running_eval_table = gr.components.Dataframe( | |
| value=running_eval_queue_df, | |
| headers=EVAL_COLS, | |
| datatype=EVAL_TYPES, | |
| max_rows=5, | |
| ) | |
| with gr.Accordion( | |
| f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})", | |
| open=False, | |
| ): | |
| with gr.Row(): | |
| pending_eval_table = gr.components.Dataframe( | |
| value=pending_eval_queue_df, | |
| headers=EVAL_COLS, | |
| datatype=EVAL_TYPES, | |
| max_rows=5, | |
| ) | |
| with gr.Row(): | |
| gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text") | |
| with gr.Row(): | |
| with gr.Column(): | |
| model_name_textbox = gr.Textbox(label="Model name") | |
| revision_name_textbox = gr.Textbox(label="revision", placeholder="main") | |
| private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC) | |
| model_type = gr.Dropdown( | |
| choices=[ | |
| ModelType.PT.to_str(" : "), | |
| ModelType.FT.to_str(" : "), | |
| ModelType.IFT.to_str(" : "), | |
| ModelType.RL.to_str(" : "), | |
| ], | |
| label="Model type", | |
| multiselect=False, | |
| value=None, | |
| interactive=True, | |
| ) | |
| with gr.Column(): | |
| precision = gr.Dropdown( | |
| choices=["float16", "bfloat16", "8bit (LLM.int8)", "4bit (QLoRA / FP4)", "GPTQ"], | |
| label="Precision", | |
| multiselect=False, | |
| value="float16", | |
| interactive=True, | |
| ) | |
| weight_type = gr.Dropdown( | |
| choices=["Original", "Delta", "Adapter"], | |
| label="Weights type", | |
| multiselect=False, | |
| value="Original", | |
| interactive=True, | |
| ) | |
| base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)") | |
| submit_button = gr.Button("Submit Eval") | |
| submission_result = gr.Markdown() | |
| submit_button.click( | |
| add_new_eval, | |
| [ | |
| model_name_textbox, | |
| base_model_name_textbox, | |
| revision_name_textbox, | |
| precision, | |
| private, | |
| weight_type, | |
| model_type, | |
| ], | |
| submission_result, | |
| ) | |
| with gr.Row(): | |
| with gr.Accordion("📙 Citation", open=False): | |
| citation_button = gr.Textbox( | |
| value=CITATION_BUTTON_TEXT, | |
| label=CITATION_BUTTON_LABEL, | |
| lines=20, | |
| elem_id="citation-button", | |
| show_copy_button=True, | |
| ) | |
| dummy = gr.Textbox(visible=False) | |
| demo.load( | |
| change_tab, | |
| dummy, | |
| tabs, | |
| _js=get_window_url_params, | |
| ) | |
| scheduler = BackgroundScheduler() | |
| scheduler.add_job(restart_space, "interval", seconds=1800) | |
| scheduler.start() | |
| demo.queue(concurrency_count=40).launch() | |