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| import subprocess | |
| import gradio as gr | |
| import pandas as pd | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| from huggingface_hub import snapshot_download | |
| from src.about import ( | |
| CITATION_BUTTON_LABEL, | |
| CITATION_BUTTON_TEXT, | |
| EVALUATION_QUEUE_TEXT, | |
| INTRODUCTION_TEXT, | |
| LLM_BENCHMARKS_TEXT, | |
| TITLE, | |
| ) | |
| from src.display.css_html_js import custom_css | |
| from src.display.utils import ( | |
| BENCHMARK_COLS, | |
| COLS, | |
| EVAL_COLS, | |
| EVAL_TYPES, | |
| NUMERIC_INTERVALS, | |
| TYPES, | |
| AutoEvalColumn, | |
| ModelType, | |
| fields, | |
| WeightType, | |
| Precision | |
| ) | |
| from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN | |
| from src.populate import get_evaluation_queue_df, get_leaderboard_df | |
| from src.submission.submit import add_new_eval | |
| def restart_space(): | |
| API.restart_space(repo_id=REPO_ID) | |
| try: | |
| print(EVAL_REQUESTS_PATH) | |
| snapshot_download( | |
| repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN | |
| ) | |
| except Exception: | |
| restart_space() | |
| try: | |
| print(EVAL_RESULTS_PATH) | |
| snapshot_download( | |
| repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN | |
| ) | |
| except Exception: | |
| restart_space() | |
| raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS) | |
| leaderboard_df = original_df.copy() | |
| ( | |
| finished_eval_queue_df, | |
| running_eval_queue_df, | |
| pending_eval_queue_df, | |
| ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) | |
| # 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.model.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] | |
| ] | |
| return filtered_df | |
| def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame: | |
| 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] == True] | |
| type_emoji = [t[0] for t in type_query] | |
| filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)] | |
| filtered_df = filtered_df.loc[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 | |
| def get_data_totale(): | |
| dataset = pd.read_csv("leaderboard_general.csv", sep=',') | |
| if 'model ' in dataset.columns: | |
| dataset.rename(columns={'model ': 'model'}, inplace=True) | |
| return dataset | |
| 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.name | |
| for c in fields(AutoEvalColumn) | |
| if not c.hidden and not c.never_hidden | |
| ], | |
| value=[ | |
| c.name | |
| for c in fields(AutoEvalColumn) | |
| if c.displayed_by_default and not c.hidden and not c.never_hidden | |
| ], | |
| label="Select columns to show", | |
| elem_id="column-select", | |
| interactive=True, | |
| ) | |
| with gr.Row(): | |
| deleted_models_visibility = gr.Checkbox( | |
| value=False, 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=[t.to_str() for t in ModelType], | |
| value=[t.to_str() for t in ModelType], | |
| interactive=True, | |
| elem_id="filter-columns-type", | |
| ) | |
| filter_columns_precision = gr.CheckboxGroup( | |
| label="Precision", | |
| choices=[i.value.name for i in Precision], | |
| value=[i.value.name for i in Precision], | |
| 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[ | |
| [c.name for c in fields(AutoEvalColumn) if c.never_hidden] | |
| + shown_columns.value | |
| ], | |
| headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value, | |
| datatype=TYPES, | |
| 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[COLS], | |
| headers=COLS, | |
| datatype=TYPES, | |
| 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, | |
| ) | |
| for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility]: | |
| selector.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('Classifica RAG'): | |
| gr.Markdown('''# Classifica RAG degli LLM italiani''') | |
| gr.Markdown(f'''In questa sezione i modelli sono valutati su dei task di Q&A e ordinati per F1 Score e EM (Exact Match). La repo di riferimento è [questa](https://github.com/C080/open-llm-ita-leaderboard). | |
| I modelli in cima alla classifica sono ritenuti preferibili per i task di Retrieval Augmented Generation.''') | |
| gr.Dataframe(pd.read_csv('leaderboard.csv', sep=';')) | |
| gr.Markdown(f"Si ringrazia il @galatolo per il codice dell'eval.") | |
| with gr.TabItem('Eval aggiuntive'): | |
| gr.Markdown('''# Altre evaluation''') | |
| gr.Markdown('''Qui ci sono altri test di altri modelli valutati su molte metriche''') | |
| gr.Dataframe(get_data_totale) | |
| 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, | |
| row_count=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, | |
| row_count=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, | |
| row_count=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 commit", placeholder="main") | |
| model_type = gr.Dropdown( | |
| choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown], | |
| label="Model type", | |
| multiselect=False, | |
| value=None, | |
| interactive=True, | |
| ) | |
| with gr.Column(): | |
| precision = gr.Dropdown( | |
| choices=[i.value.name for i in Precision if i != Precision.Unknown], | |
| label="Precision", | |
| multiselect=False, | |
| value="float16", | |
| interactive=True, | |
| ) | |
| weight_type = gr.Dropdown( | |
| choices=[i.value.name for i in WeightType], | |
| 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, | |
| 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, | |
| ) | |
| scheduler = BackgroundScheduler() | |
| scheduler.add_job(restart_space, "interval", seconds=1800) | |
| scheduler.start() | |
| demo.queue(default_concurrency_limit=40).launch() | |
| # import gradio as gr | |
| # import pandas as pd | |
| # csv_filename = 'leaderboard.csv' | |
| # # url = 'https://docs.google.com/spreadsheets/d/1Oh3nrbdWjKuh9twJsc9yJLppiJeD_BZyKgCTOxRkALM/export?format=csv' | |
| # def get_data_classifica(): | |
| # dataset = pd.read_csv("leaderboard_general.csv", sep=',') | |
| # if 'model ' in dataset.columns: | |
| # dataset.rename(columns={'model ': 'model'}, inplace=True) | |
| # df_classifica = dataset[['model', 'helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']] | |
| # df_classifica['media'] = df_classifica[['helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']].mean(axis=1) | |
| # df_classifica['media'] = df_classifica['media'].round(3) | |
| # df_classifica = df_classifica.sort_values(by='media', ascending=False) | |
| # df_classifica = df_classifica[['model', 'media', 'helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']] | |
| # return df_classifica | |
| # def get_data_totale(): | |
| # dataset = pd.read_csv("leaderboard_general.csv", sep=',') | |
| # if 'model ' in dataset.columns: | |
| # dataset.rename(columns={'model ': 'model'}, inplace=True) | |
| # return dataset | |
| # with gr.Blocks() as demo: | |
| # with gr.Tab('Classifica Generale'): | |
| # gr.Markdown('''# Classifica generale degli LLM italiani''') | |
| # discord_link = 'https://discord.gg/m7sS3mduY2' | |
| # gr.Markdown(''' | |
| # I modelli sottostanti sono stati testati con [lm_evaluation_harness](https://github.com/EleutherAI/lm-evaluation-harness) su task specifici per l'italiano introdotti con questa [PR](https://github.com/EleutherAI/lm-evaluation-harness/pull/1358). | |
| # L'intero progetto, i modelli e i dataset sono rigorosamente open source e tutti i risultati sono riproducibili lanciando i seguenti comandi: | |
| # ``` | |
| # lm_eval --model hf --model_args pretrained=HUGGINGFACE_MODEL_ID --tasks hellaswag_it,arc_it --device cuda:0 --batch_size auto:2 | |
| # ``` | |
| # ``` | |
| # lm_eval --model hf --model_args pretrained=HUGGINGFACE_MODEL_ID --tasks m_mmlu_it --num_fewshot 5 --device cuda:0 --batch_size auto:2 | |
| # ``` | |
| # ''') | |
| # gr.DataFrame(get_data_classifica, every=3600) | |
| # gr.Markdown(f"Contributore principale: @giux78") | |
| # gr.Markdown(''' | |
| # ### Risultati su modelli "internazionali" (instruct) | |
| # | Model | Arc-c | HellaS | MMUL | AVG | | |
| # | --- | --- | --- | --- | --- | | |
| # | Mixtral 8x22b | 55.3 | 77.1 | 75.8 | 69.4 | | |
| # | LLama3 70b | 52.9 | 70.3 | 74.8 | 66.0 | | |
| # | command-r-plus | 49.5 | 74.9 | 67.6 | 64.0 | | |
| # | Mixtral 8x7b | 51.1 | 72.9 | 65.9 | 63.3 | | |
| # | LLama2 70b | 49.4 | 70.9 | 65.1 | 61.8 | | |
| # | command-r-v01 | 50.8 | 72.3 | 60.0 | 61.0 | | |
| # | Phi-3-mini | 43.46 | 61.44 | 56.55 | 53.8 | | |
| # | LLama3 8b | 44.3 | 59.9 | 55.7 | 53.3 | | |
| # | LLama1 34b | 42.9 | 65.4 | 49.0 | 52.4 | | |
| # | Mistral 7b | 41.49 | 61.22 | 52.53 | 51.7 | | |
| # | Gemma 1.1 7b | 41.75 | 54.07 | 49.45 | 48.4 | | |
| # ''') | |
| # with gr.Tab('Classifica RAG'): | |
| # gr.Markdown('''# Classifica RAG degli LLM italiani''') | |
| # gr.Markdown(f'''In questa sezione i modelli sono valutati su dei task di Q&A e ordinati per F1 Score e EM (Exact Match). La repo di riferimento è [questa](https://github.com/C080/open-llm-ita-leaderboard). | |
| # I modelli in cima alla classifica sono ritenuti preferibili per i task di Retrieval Augmented Generation.''') | |
| # gr.Dataframe(pd.read_csv(csv_filename, sep=';')) | |
| # gr.Markdown(f"Si ringrazia il @galatolo per il codice dell'eval.") | |
| # with gr.Tab('Eval aggiuntive'): | |
| # gr.Markdown('''# Altre evaluation''') | |
| # gr.Markdown('''Qui ci sono altri test di altri modelli, che non sono ancora stati integrati nella classifica generale.''') | |
| # gr.DataFrame(get_data_totale, every=3600) | |
| # with gr.Tab('Informazioni'): | |
| # form_link = "https://forms.gle/Gc9Dfu52xSBhQPpAA" | |
| # gr.Markdown('''# Community discord | |
| # Se vuoi contribuire al progetto o semplicemente unirti alla community di LLM italiani unisciti al nostro [discord!](https://discord.gg/m7sS3mduY2) | |
| # # Aggiungi il tuo modello | |
| # Se hai sviluppato un tuo modello che vuoi far valutare, compila il form [qui](https://forms.gle/Gc9Dfu52xSBhQPpAA) è tutto gratuito! | |
| # ''') | |
| # with gr.Tab('Sponsor'): | |
| # gr.Markdown(''' | |
| # # Sponsor | |
| # Le evaluation della classifica generale sono state gentilmente offerte da un provider cloud italiano [seeweb.it](https://www.seeweb.it/) specializzato in servizi di GPU cloud e AI. | |
| # ''') | |
| # demo.launch() |