Upload app.py
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CultriX
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app.py
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import re
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import streamlit as st
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import requests
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@@ -6,11 +7,35 @@ from io import StringIO
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import plotly.graph_objs as go
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from huggingface_hub import HfApi
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from huggingface_hub.utils import RepositoryNotFoundError, RevisionNotFoundError
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from yall import create_yall
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def convert_markdown_table_to_dataframe(md_content):
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"""
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Converts markdown table to Pandas DataFrame, handling special characters and links,
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@@ -59,8 +84,7 @@ def get_model_info(df):
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return df
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def create_bar_chart(df, category):
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"""Create and display a bar chart for a given category."""
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st.write(f"### {category} Scores")
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@@ -73,7 +97,7 @@ def create_bar_chart(df, category):
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x=sorted_df[category],
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y=sorted_df['Model'],
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orientation='h',
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marker=dict(color=sorted_df[category], colorscale='
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))
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# Update layout for better readability
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)
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# Adjust the height of the chart based on the number of rows in the DataFrame
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st.plotly_chart(fig, use_container_width=True, height=35)
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# Example usage:
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# create_bar_chart(your_dataframe, 'Your_Category')
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def main():
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st.set_page_config(page_title="YALL - Yet Another LLM Leaderboard", layout="wide")
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st.title("π YALL - Yet Another LLM Leaderboard")
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st.markdown("Leaderboard made with π§ [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) using [Nous](https://huggingface.co/NousResearch) benchmark suite.")
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content = create_yall()
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tab1, tab2 = st.tabs(["π Leaderboard", "π About"])
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# Display dataframe
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full_df = convert_markdown_table_to_dataframe(content)
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for col in score_columns:
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# Corrected use of pd.to_numeric
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full_df[col] = pd.to_numeric(full_df[col].str.strip(), errors='coerce')
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full_df = get_model_info(full_df)
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full_df['Tags'] = full_df['Tags'].fillna('')
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df = pd.DataFrame(columns=full_df.columns)
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# Toggles
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with col2:
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show_mistral = st.checkbox("Mistral (7B)", value=True)
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with col3:
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show_other = st.checkbox("Other", value=True)
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# Create a DataFrame based on selected filters
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dfs_to_concat = []
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if dfs_to_concat:
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df = pd.concat(dfs_to_concat, ignore_index=True)
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# Sort values
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df = df.sort_values(by='Average', ascending=False)
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# Add a search bar
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search_query = st.text_input("Search models", "")
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"URL": st.column_config.LinkColumn("URL"),
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},
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hide_index=True,
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height=
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)
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# Add a button to export data to CSV
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if st.button("Export to CSV"):
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# Export the DataFrame to CSV
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with tab2:
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st.markdown('''
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### Nous benchmark suite
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Popularized by [Teknium](https://huggingface.co/teknium) and [NousResearch](https://huggingface.co/NousResearch), this benchmark suite aggregates four benchmarks:
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* [**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`
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* **GPT4ALL** (0-shot): `hellaswag,openbookqa,winogrande,arc_easy,arc_challenge,boolq,piqa`
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* [**TruthfulQA**](https://arxiv.org/abs/2109.07958) (0-shot): `truthfulqa_mc`
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* [**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`
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### Reproducibility
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You can easily reproduce these results using π§ [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).
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### Clone this space
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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:
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* Change the `gist_id` in [yall.py](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard/blob/main/yall.py#L126).
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* Create "New Secret" in Settings > Variables and secrets (name: "github", value: [your GitHub token](https://github.com/settings/tokens))
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A special thanks to [gblazex](https://huggingface.co/gblazex) for providing many evaluations
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''')
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if __name__ == "__main__":
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main()
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# Importing necessary libraries
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import re
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import streamlit as st
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import requests
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import plotly.graph_objs as go
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from huggingface_hub import HfApi
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from huggingface_hub.utils import RepositoryNotFoundError, RevisionNotFoundError
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from yall import create_yall
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from functools import cache
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# Function to get model info from Hugging Face API using caching
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@cache
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def cached_model_info(api, model):
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try:
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return api.model_info(repo_id=str(model))
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except (RepositoryNotFoundError, RevisionNotFoundError):
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return None
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# Function to get model info from DataFrame and update it with likes and tags
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@st.cache
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def get_model_info(df):
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api = HfApi()
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for index, row in df.iterrows():
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model_info = cached_model_info(api, row['Model'].strip())
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if model_info:
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df.loc[index, 'Likes'] = model_info.likes
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df.loc[index, 'Tags'] = ', '.join(model_info.tags)
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else:
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df.loc[index, 'Likes'] = -1
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df.loc[index, 'Tags'] = ''
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return df
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# Function to convert markdown table to DataFrame and extract Hugging Face URLs
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def convert_markdown_table_to_dataframe(md_content):
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"""
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Converts markdown table to Pandas DataFrame, handling special characters and links,
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return df
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# Function to create bar chart for a given category
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def create_bar_chart(df, category):
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"""Create and display a bar chart for a given category."""
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st.write(f"### {category} Scores")
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x=sorted_df[category],
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y=sorted_df['Model'],
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orientation='h',
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marker=dict(color=sorted_df[category], colorscale='Spectral') # You can change 'Viridis' to another color scale
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))
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# Update layout for better readability
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)
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# Adjust the height of the chart based on the number of rows in the DataFrame
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st.plotly_chart(fig, use_container_width=True, height=len(df) * 35)
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# Main function to run the Streamlit app
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def main():
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# Set page configuration and title
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st.set_page_config(page_title="YALL - Yet Another LLM Leaderboard", layout="wide")
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st.title("π YALL - Yet Another LLM Leaderboard")
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st.markdown("Leaderboard made with π§ [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) using [Nous](https://huggingface.co/NousResearch) benchmark suite.")
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# Create tabs for leaderboard and about section
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content = create_yall()
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tab1, tab2 = st.tabs(["π Leaderboard", "π About"])
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# Display dataframe
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full_df = convert_markdown_table_to_dataframe(content)
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for col in score_columns:
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# Corrected use of pd.to_numeric
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full_df[col] = pd.to_numeric(full_df[col].str.strip(), errors='coerce')
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full_df = get_model_info(full_df)
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full_df['Tags'] = full_df['Tags'].fillna('')
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df = pd.DataFrame(columns=full_df.columns)
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# Toggles for filtering by tags
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show_phi = st.checkbox("Phi (2.8B)", value=True)
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show_mistral = st.checkbox("Mistral (7B)", value=True)
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show_other = st.checkbox("Other", value=True)
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# Create a DataFrame based on selected filters
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dfs_to_concat = []
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if dfs_to_concat:
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df = pd.concat(dfs_to_concat, ignore_index=True)
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# Add a search bar
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search_query = st.text_input("Search models", "")
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"URL": st.column_config.LinkColumn("URL"),
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},
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hide_index=True,
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height=len(df) * 37,
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)
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selected_models = st.multiselect('Select models to compare', df['Model'].unique())
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comparison_df = df[df['Model'].isin(selected_models)]
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st.dataframe(comparison_df)
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# Add a button to export data to CSV
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if st.button("Export to CSV"):
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# Export the DataFrame to CSV
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with tab2:
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st.markdown('''
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### Nous benchmark suite
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Popularized by [Teknium](https://huggingface.co/teknium) and [NousResearch](https://huggingface.co/NousResearch), this benchmark suite aggregates four benchmarks:
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+
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* [**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`
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* **GPT4ALL** (0-shot): `hellaswag,openbookqa,winogrande,arc_easy,arc_challenge,boolq,piqa`
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* [**TruthfulQA**](https://arxiv.org/abs/2109.07958) (0-shot): `truthfulqa_mc`
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* [**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`
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### Reproducibility
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You can easily reproduce these results using π§ [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).
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### Clone this space
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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:
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* Change the `gist_id` in [yall.py](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard/blob/main/yall.py#L126).
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* Create "New Secret" in Settings > Variables and secrets (name: "github", value: [your GitHub token](https://github.com/settings/tokens))
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A special thanks to [gblazex](https://huggingface.co/gblazex) for providing many evaluations.
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''')
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# Run the main function if this script is run directly
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if __name__ == "__main__":
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main()
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