import streamlit as st import pandas as pd from huggingface_hub import HfApi from huggingface_hub.utils import RepositoryNotFoundError, RevisionNotFoundError from itertools import combinations import re from functools import cache from io import StringIO from yall import create_yall import plotly.graph_objs as go def calculate_pages(df, items_per_page): return -(-len(df) // items_per_page) # Equivalent to math.ceil(len(df) / items_per_page) # Function to get model info from Hugging Face API using caching @cache def cached_model_info(api, model): try: return api.model_info(repo_id=str(model)) except (RepositoryNotFoundError, RevisionNotFoundError): return None # Function to get model info from DataFrame and update it with likes and tags @st.cache def get_model_info(df): api = HfApi() for index, row in df.iterrows(): model_info = cached_model_info(api, row['Model'].strip()) if model_info: df.loc[index, 'Likes'] = model_info.likes df.loc[index, 'Tags'] = ', '.join(model_info.tags) else: df.loc[index, 'Likes'] = -1 df.loc[index, 'Tags'] = '' return df # Function to convert markdown table to DataFrame and extract Hugging Face URLs def convert_markdown_table_to_dataframe(md_content): """ Converts markdown table to Pandas DataFrame, handling special characters and links, extracts Hugging Face URLs, and adds them to a new column. """ # Remove leading and trailing | characters cleaned_content = re.sub(r'\|\s*$', '', re.sub(r'^\|\s*', '', md_content, flags=re.MULTILINE), flags=re.MULTILINE) # Create DataFrame from cleaned content df = pd.read_csv(StringIO(cleaned_content), sep="\|", engine='python') # Remove the first row after the header df = df.drop(0, axis=0) # Strip whitespace from column names df.columns = df.columns.str.strip() # Extract Hugging Face URLs and add them to a new column model_link_pattern = r'\[(.*?)\]\((.*?)\)\s*\[.*?\]\(.*?\)' df['URL'] = df['Model'].apply(lambda x: re.search(model_link_pattern, x).group(2) if re.search(model_link_pattern, x) else None) # Clean Model column to have only the model link text df['Model'] = df['Model'].apply(lambda x: re.sub(model_link_pattern, r'\1', x)) return df @st.cache_data def get_model_info(df): api = HfApi() # Initialize new columns for likes and tags df['Likes'] = None df['Tags'] = None # Iterate through DataFrame rows for index, row in df.iterrows(): model = row['Model'].strip() try: model_info = api.model_info(repo_id=str(model)) df.loc[index, 'Likes'] = model_info.likes df.loc[index, 'Tags'] = ', '.join(model_info.tags) except (RepositoryNotFoundError, RevisionNotFoundError): df.loc[index, 'Likes'] = -1 df.loc[index, 'Tags'] = '' return df #def calculate_highest_combined_score(data, column): # score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench'] # # Ensure the column exists and has numeric data # if column not in data.columns or not pd.api.types.is_numeric_dtype(data[column]): # return column, {} # scores = data[column].dropna().tolist() # models = data['Model'].tolist() # top_combinations = {r: [] for r in range(2, 5)} # for r in range(2, 5): # for combination in combinations(zip(scores, models), r): # combined_score = sum(score for score, _ in combination) # top_combinations[r].append((combined_score, tuple(model for _, model in combination))) # top_combinations[r].sort(key=lambda x: x[0], reverse=True) # top_combinations[r] = top_combinations[r][:5] # return column, top_combinations ## Modified function to display the results of the highest combined scores using st.dataframe #def display_highest_combined_scores(data): # score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench'] # with st.spinner('Calculating highest combined scores...'): # results = [calculate_highest_combined_score(data, col) for col in score_columns] # for column, top_combinations in results: # st.subheader(f"Top Combinations for {column}") # for r, combinations in top_combinations.items(): # # Prepare data for DataFrame # rows = [{'Score': score, 'Models': ', '.join(combination)} for score, combination in combinations] # df = pd.DataFrame(rows) # # # Display using st.dataframe # st.markdown(f"**Number of Models: {r}**") # st.dataframe(df, height=150) # Adjust height as necessary # Function to create bar chart for a given category def create_bar_chart(df, category): """Create and display a bar chart for a given category.""" st.write(f"### {category} Scores") # Sort the DataFrame based on the category score sorted_df = df[['Model', category]].sort_values(by=category, ascending=True) # Create the bar chart with a color gradient (using 'Viridis' color scale as an example) fig = go.Figure(go.Bar( x=sorted_df[category], y=sorted_df['Model'], orientation='h', marker=dict(color=sorted_df[category], colorscale='Spectral') # You can change 'Viridis' to another color scale )) # Update layout for better readability fig.update_layout( margin=dict(l=20, r=20, t=20, b=20) ) # Adjust the height of the chart based on the number of rows in the DataFrame st.plotly_chart(fig, use_container_width=True, height=len(df) * 35) # Main function to run the Streamlit app def main(): # Set page configuration and title st.set_page_config(page_title="YALL - Yet Another LLM Leaderboard", layout="wide") st.title("🏆 YALL - Yet Another LLM Leaderboard") st.markdown("Leaderboard made with 🧐 [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) using [Nous](https://huggingface.co/NousResearch) benchmark suite.") # Create tabs for leaderboard and about section content = create_yall() tab1, tab2 = st.tabs(["🏆 Leaderboard", "📝 About"]) # Leaderboard tab with tab1: if content: try: score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench'] # Display dataframe full_df = convert_markdown_table_to_dataframe(content) for col in score_columns: # Corrected use of pd.to_numeric full_df[col] = pd.to_numeric(full_df[col].str.strip(), errors='coerce') full_df = get_model_info(full_df) full_df['Tags'] = full_df['Tags'].fillna('') df = pd.DataFrame(columns=full_df.columns) # Toggles for filtering by tags show_phi = st.checkbox("Phi (2.8B)", value=True) show_mistral = st.checkbox("Mistral (7B)", value=True) show_other = st.checkbox("Other", value=True) # Create a DataFrame based on selected filters dfs_to_concat = [] if show_phi: dfs_to_concat.append(full_df[full_df['Tags'].str.lower().str.contains('phi,|phi-msft,')]) if show_mistral: dfs_to_concat.append(full_df[full_df['Tags'].str.lower().str.contains('mistral,')]) if show_other: other_df = full_df[~full_df['Tags'].str.lower().str.contains('phi,|phi-msft,|mistral,')] dfs_to_concat.append(other_df) # Concatenate the DataFrames if dfs_to_concat: df = pd.concat(dfs_to_concat, ignore_index=True) # Add a search bar search_query = st.text_input("Search models", "") # Filter the DataFrame based on the search query if search_query: df = df[df['Model'].str.contains(search_query, case=False)] # Add a selectbox for page selection items_per_page = 30 pages = calculate_pages(df, items_per_page) page = st.selectbox("Page", list(range(1, pages + 1))) # Sort the DataFrame by 'Average' column in descending order df = df.sort_values(by='Average', ascending=False) # Slice the DataFrame based on the selected page start = (page - 1) * items_per_page end = start + items_per_page df = df[start:end] # Display the filtered DataFrame or the entire leaderboard st.dataframe( df[['Model'] + score_columns + ['Likes', 'URL']], use_container_width=True, column_config={ "Likes": st.column_config.NumberColumn( "Likes", help="Number of likes on Hugging Face", format="%d ❤️", ), "URL": st.column_config.LinkColumn("URL"), }, hide_index=True, height=len(df) * 37, ) selected_models = st.multiselect('Select models to compare', df['Model'].unique()) comparison_df = df[df['Model'].isin(selected_models)] st.dataframe(comparison_df) # Add a button to export data to CSV if st.button("Export to CSV"): # Export the DataFrame to CSV csv_data = full_df.to_csv(index=False) # Create a link to download the CSV file st.download_button( label="Download CSV", data=csv_data, file_name="leaderboard.csv", key="download-csv", help="Click to download the CSV file", ) # Full-width plot for the first category create_bar_chart(df, score_columns[0]) # Next two plots in two columns col1, col2 = st.columns(2) with col1: create_bar_chart(df, score_columns[1]) with col2: create_bar_chart(df, score_columns[2]) # Last two plots in two columns col3, col4 = st.columns(2) with col3: create_bar_chart(df, score_columns[3]) with col4: create_bar_chart(df, score_columns[4]) # display_highest_combined_scores(full_df) # Call to display the calculated scores except Exception as e: st.error("An error occurred while processing the markdown table.") st.error(str(e)) else: st.error("Failed to download the content from the URL provided.") # About tab with tab2: st.markdown(''' ### Nous benchmark suite Popularized by [Teknium](https://huggingface.co/teknium) and [NousResearch](https://huggingface.co/NousResearch), this benchmark suite aggregates four benchmarks: * [**AGIEval**](https://arxiv.org/abs/2304.06364) (0-shot): `agieval_aqua_rat,agieval_logiqa_en,agieval_lsat_ar,agieval_lsat_lr,agieval_lsat_rc,agieval_sat_en,agieval_sat_en_without_passage,agieval_sat_math` * **GPT4ALL** (0-shot): `hellaswag,openbookqa,winogrande,arc_easy,arc_challenge,boolq,piqa` * [**TruthfulQA**](https://arxiv.org/abs/2109.07958) (0-shot): `truthfulqa_mc` * [**Bigbench**](https://arxiv.org/abs/2206.04615) (0-shot): `bigbench_causal_judgement,bigbench_date_understanding,bigbench_disambiguation_qa,bigbench_geometric_shapes,bigbench_logical_deduction_five_objects,bigbench_logical_deduction_seven_objects,bigbench_logical_deduction_three_objects,bigbench_movie_recommendation,bigbench_navigate,bigbench_reasoning_about_colored_objects,bigbench_ruin_names,bigbench_salient_translation_error_detection,bigbench_snarks,bigbench_sports_understanding,bigbench_temporal_sequences,bigbench_tracking_shuffled_objects_five_objects,bigbench_tracking_shuffled_objects_seven_objects,bigbench_tracking_shuffled_objects_three_objects` ### Reproducibility You can easily reproduce these results using 🧐 [LLM AutoEval](https://github.com/mlabonne/llm-autoeval/tree/master), a colab notebook that automates the evaluation process (benchmark: `nous`). This will upload the results to GitHub as gists. You can find the entire table with the links to the detailed results [here](https://gist.github.com/mlabonne/90294929a2dbcb8877f9696f28105fdf). ### Clone this space You can create your own leaderboard with your LLM AutoEval results on GitHub Gist. You just need to clone this space and specify two variables: * Change the `gist_id` in [yall.py](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard/blob/main/yall.py#L126). * Create "New Secret" in Settings > Variables and secrets (name: "github", value: [your GitHub token](https://github.com/settings/tokens)) A special thanks to [gblazex](https://huggingface.co/gblazex) for providing many evaluations. ''') # Run the main function if this script is run directly if __name__ == "__main__": main()