Spaces:
Running
Running
| """A gradio app that renders a static leaderboard. This is used for Hugging Face Space.""" | |
| import argparse | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import gradio as gr | |
| import pandas as pd | |
| import json | |
| from constants import BANNER, INTRODUCTION_TEXT, CITATION_TEXT, METRICS_TAB_TEXT, DIR_OUTPUT_REQUESTS | |
| LAST_UPDATED = "Feb 28th 2024" | |
| css = """ | |
| .markdown-text{font-size: 15pt} | |
| .markdown-text-small{font-size: 13pt} | |
| th { | |
| text-align: center; | |
| } | |
| td { | |
| font-size: 15px; /* Adjust the font size as needed */ | |
| text-align: center; | |
| } | |
| #od-benchmark-tab-table-button{ | |
| font-size: 15pt; | |
| font-weight: bold; | |
| } | |
| """ | |
| column_names = { | |
| "model": "Model", | |
| "Overall": "All 🎯", | |
| "Turn 1": "Turn 1️⃣", | |
| "Turn 2": "Turn 2️⃣", | |
| } | |
| model_info = { | |
| "gpt-4": {"hf_name": "https://platform.openai.com/", "pretty_name": "gpt-4"}, | |
| "gpt-3.5-turbo": {"hf_name": "https://platform.openai.com/", "pretty_name": "gpt-3.5-turbo"}, | |
| "Llama-2-70b-hf": {"hf_name": "meta-llama/Llama-2-70b-hf", "pretty_name": "Llama-2-70B"}, | |
| "Llama-2-13b-hf": {"hf_name": "meta-llama/Llama-2-13b-hf", "pretty_name": "Llama-2-13B"}, | |
| "Llama-2-7b-hf": {"hf_name": "meta-llama/Llama-2-7b-hf", "pretty_name": "Llama-2-7B"}, | |
| "Mixtral-8x7B-v0.1": {"hf_name": "mistralai/Mixtral-8x7B-v0.1", "pretty_name": "Mixtral-8x7B"}, | |
| "Mistral-7b-v0.1": {"hf_name": "mistralai/Mistral-7B-v0.1", "pretty_name": "Mistral-7B v0.1"}, | |
| "Mistral-7b-v0.2": {"hf_name": "mistralai/Mistral-7B-v0.1", "pretty_name": "Mistral-7B v0.2"}, | |
| "Yi-34B": {"hf_name": "01-ai/Yi-34B", "pretty_name": "Yi-34B"}, | |
| "Yi-6B": {"hf_name": "01-ai/Yi-6B", "pretty_name": "Yi-6B"}, | |
| "gemma-7b": {"hf_name": "google/gemma-7b", "pretty_name": "Gemma-7B"}, | |
| "gemma-2b": {"hf_name": "google/gemma-2b", "pretty_name": "Gemma-2B"}, | |
| "phi-2": {"hf_name": "microsoft/phi-2", "pretty_name": "Phi-2 @hf"}, | |
| "olmo": {"hf_name": "allenai/OLMo-7B", "pretty_name": "OLMo-7B @hf"}, | |
| "phi-2-vllm": {"hf_name": "microsoft/phi-2", "pretty_name": "Phi-2 (2.7B)"}, | |
| "olmo-7b-vllm": {"hf_name": "allenai/OLMo-7B", "pretty_name": "OLMo-7B"}, | |
| "falcon-7b": {"hf_name": "microsoft/falcon-7b", "pretty_name": "Falcon-7B"}, | |
| "mpt-7b": {"hf_name": "mosaicml/mpt-7b", "pretty_name": "MPT-7B"}, | |
| "amber": {"hf_name": "LLM360/Amber", "pretty_name": "Amber (7B)"}, | |
| "dbrx": {"hf_name": "databricks/dbrx-base", "pretty_name": "DBRX-base"}, | |
| } | |
| def formatter(x): | |
| x = round(x, 2) | |
| return x | |
| def make_clickable_model(model_name, model_info): | |
| if model_info[model_name]['hf_name'].startswith("http"): | |
| link = model_info[model_name]['hf_name'] | |
| else: | |
| link = f"https://huggingface.co/{model_info[model_name]['hf_name']}" | |
| if model_name.startswith("gpt"): | |
| return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted; background-color: lightgray;">{model_info[model_name]["pretty_name"]}</a>' | |
| else: | |
| return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_info[model_name]["pretty_name"]}</a>' | |
| def build_demo(original_df, full_df, TYPES): | |
| with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo: | |
| # gr.HTML(BANNER, elem_id="banner") | |
| gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") | |
| with gr.Tabs(elem_classes="tab-buttons") as tabs: | |
| with gr.TabItem("🏅 Leaderboard", elem_id="od-benchmark-tab-table", id=0): | |
| leaderboard_table = gr.components.Dataframe( | |
| value=original_df, | |
| datatype=TYPES, | |
| height=1000, | |
| wrap=False, | |
| elem_id="leaderboard-table", | |
| interactive=False, | |
| visible=True, | |
| min_width=60, | |
| ) | |
| with gr.TabItem("🐑 URIAL + 🤗 OpenLLM", elem_id="od-benchmark-tab-table", id=1): | |
| gr.Markdown("### More results from the awesome 🤗 [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) ", elem_classes="markdown-text") | |
| leaderboard_table_full = gr.components.Dataframe( | |
| value=full_df, | |
| datatype=TYPES, | |
| height=1000, | |
| wrap=False, | |
| elem_id="leaderboard-table-full", | |
| interactive=False, | |
| visible=True, | |
| min_width=60, | |
| ) | |
| gr.Markdown(f"Last updated on **{LAST_UPDATED}**", elem_classes="markdown-text-small") | |
| with gr.Row(): | |
| with gr.Accordion("📙 Citation", open=False): | |
| gr.Textbox( | |
| value=CITATION_TEXT, lines=18, | |
| label="Copy the BibTeX to cite URIAL and MT-Bench", | |
| elem_id="citation-button", | |
| show_copy_button=True) | |
| # ).style(show_copy_button=True) | |
| return demo | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--share", action="store_true") | |
| parser.add_argument("--result_file", help="Path to results table", default="leaderboard_data.jsonl") | |
| args = parser.parse_args() | |
| all_model_hf_ids = {v["hf_name"]: k for k, v in model_info.items()} | |
| # Load Open LLM Leaderboard | |
| with open("open-llm-leaderboard.json") as f: | |
| open_llm_leaderbaord = json.load(f) | |
| full_leaderboard = {} | |
| for item in open_llm_leaderbaord: | |
| if item["Model"] in all_model_hf_ids: | |
| # print(item["Model"]) | |
| # print(item["Average \u2b06\ufe0f"]) | |
| full_bench_item = {} | |
| # full_bench_item["hf_name"] = item["Model"] | |
| full_bench_item["model_name"] = all_model_hf_ids[item["Model"]] | |
| tasks = ["HellaSwag", "ARC", "Winogrande", "TruthfulQA", "MMLU", "GSM8K"] | |
| for task in tasks: | |
| full_bench_item[task] = item[task] | |
| full_bench_item["HF_AVG"] = item["Average \u2b06\ufe0f"] | |
| full_leaderboard[all_model_hf_ids[item["Model"]]] = full_bench_item | |
| # Load URIAL Leaderboard | |
| with open("leaderboard_data.jsonl") as f: | |
| for line in f: | |
| item = json.loads(line) | |
| if item["model"] in full_leaderboard: | |
| full_leaderboard[item["model"]]["URIAL_AVG"] = item["Overall"] | |
| # Process the URIAL Benchmark Tab | |
| original_df = pd.read_json(args.result_file, lines=True) | |
| print(original_df.columns) | |
| for col in original_df.columns: | |
| if col == "model": | |
| original_df[col] = original_df[col].apply(lambda x: x.replace(x, make_clickable_model(x, model_info))) | |
| else: | |
| original_df[col] = original_df[col].apply(formatter) # For numerical values | |
| # Define the first column explicitly, add 'Overall' as the second column, and then append the rest excluding 'Overall' | |
| new_order = [original_df.columns[0], 'Overall'] + [col for col in original_df.columns if col not in [original_df.columns[0], 'Overall']] | |
| # Reorder the DataFrame columns using the new order | |
| reordered_df = original_df[new_order] | |
| reordered_df.sort_values(by='Overall', inplace=True, ascending=False) | |
| reordered_df.rename(columns=column_names, inplace=True) | |
| # Process the Full Benchmark Tab | |
| full_df = pd.DataFrame(full_leaderboard).T | |
| full_df = full_df.reset_index() | |
| full_df.rename(columns={"index": "model"}, inplace=True) | |
| full_df = full_df[["model", "URIAL_AVG", "HF_AVG", "HellaSwag", "ARC", "Winogrande", "TruthfulQA", "MMLU", "GSM8K"]] | |
| full_df.sort_values(by='URIAL_AVG', inplace=True, ascending=False) | |
| full_df["model"] = full_df["model"].apply(lambda x: make_clickable_model(x, model_info)) | |
| full_df.rename(columns=column_names, inplace=True) | |
| # apply formatter to numerical columns | |
| for col in full_df.columns: | |
| if col not in ["Model"]: | |
| full_df[col] = full_df[col].apply(formatter) # For numerical values | |
| # COLS = [c.name for c in fields(AutoEvalColumn)] | |
| # TYPES = [c.type for c in fields(AutoEvalColumn)] | |
| TYPES = ["markdown", "number"] | |
| demo = build_demo(reordered_df, full_df, TYPES) | |
| demo.launch(share=args.share) | |