import gradio as gr import subprocess import os import sys import time import pandas as pd from threading import Thread # Add the path to the "src" directory of detect-pretrain-code-contamination to the sys.path project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "detect-pretrain-code-contamination")) src_dir = os.path.join(project_root, "src") sys.path.insert(0, src_dir) import run as evaluator # Import the run module from src.css_html import custom_css from src.text_content import ABOUT_TEXT, SUBMISSION_TEXT, SUBMISSION_TEXT_2 from src.envs import API, H4_TOKEN, REPO_ID from huggingface_hub import HfApi from src.utils import ( AutoEvalColumn, fields, is_model_on_hub, make_clickable_names, styled_error, styled_message, ) 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] # CONFIGURATION: ref_model = "huggyllama/llama-7b" test_datasets = ["truthful_qa","cais/mmlu","ai2_arc","gsm8k","Rowan/hellaswag","winogrande"] modelQueue = [] def restart_space(): #Most dumbest update function to ever exist, I'm sobbing in tears as I've tried to make gradio update the leaderboard literally any other way. API.restart_space(repo_id=REPO_ID, token=H4_TOKEN) def save_to_txt(model, results, model_type): file_path = "data/code_eval_board.csv" with open(file_path, "a") as f: f.write(f"\n{model_type},{model}," + str(results["arc"]) + "," + str(results["hellaswag"]) + "," + str(results["mmlu"]) + "," + str(results["truthfulQA"]) + "," + str(results["winogrande"]) + "," + str(results["gsm8k"])) f.close() restart_space() def run_test(model,ref_model,data): print(f"|| TESTING {data} ||") return evaluator.main( target_model=f"{model}", ref_model=f"{ref_model}", output_dir="out", data=f"{data}", length=64, key_name="input", ratio_gen=0.4 ) # Call the main function in detect-pretrain-code-contamination/src/run.py def evaluate(model,model_type): global ref_model print(f"|| EVALUATING {model} ||") results = { "arc": run_test(model, ref_model, test_datasets[2]), "hellaswag": run_test(model, ref_model, test_datasets[4]), "mmlu": run_test(model, ref_model, test_datasets[1]), "truthfulQA": run_test(model, ref_model, test_datasets[0]), "winogrande": run_test(model, ref_model, test_datasets[5]), "gsm8k": run_test(model, ref_model, test_datasets[3]), "ref_model": ref_model, } # Save to .txt file in /Evaluations/{model} save_to_txt(model, results, model_type) return "\n".join([f"{k}:{results[k]}" for k in results]) def worker_thread(): global modelQueue, server while True: for submission in modelQueue: evaluate(submission[0],submission[1].split(" ")[0]) modelQueue.pop(modelQueue.index(submission)) time.sleep(1) time.sleep(1) def queue(model,model_type): global modelQueue modelQueue.append([model,model_type]) print(f"QUEUE:\n{modelQueue}") ### bigcode/bigcode-models-leaderboard def add_new_eval( model: str, revision: str, precision: str, model_type: str, ): precision = precision if model_type is None or model_type == "" or model_type == []: return styled_error("Please select a model type.") print(model_type) # check the model actually exists before adding the eval if revision == "": revision = "main" model_on_hub, error = is_model_on_hub(model, revision) if not model_on_hub: return styled_error(f'Model "{model}" {error}') print("Adding new eval") queue(model,model_type) return styled_message("Your request has been submitted to the evaluation queue!\n") def select_columns(df, columns): 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_items(df, leaderboard_table, query): if query == "All": return df[leaderboard_table.columns] else: query = query[0] # take only the emoji character filtered_df = df[(df["T"] == query)] return filtered_df[leaderboard_table.columns] def search_table(df, leaderboard_table, query): filtered_df = df[(df["Models"].str.contains(query, case=False))] return filtered_df[leaderboard_table.columns] demo = gr.Blocks(css=custom_css) with demo: with gr.Row(): gr.Markdown( """
Inspired from the 🤗 Open LLM Leaderboard and 🤗 Big Code Models Leaderboard ⭐, we use an implementation of Detecting Pretraining Data from Large Language Models paper found in this github repo, to provide contamination scores for LLMs on the datasets used by Open LLM Leaderboard.\ This space should NOT be used to flag or accuse models of cheating / being contamined, instead, it should form part of a holistic assesment by the parties involved.
""", elem_classes="markdown-text", ) with gr.Tabs(elem_classes="tab-buttons") as tabs: with gr.Column(): with gr.Tabs(elem_classes="A100-tabs") as A100_tabs: with gr.TabItem("🔍 Evaluations", id=0): with gr.Column(): with gr.Accordion("➡️ See filters", open=False): 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, ] ], value=[ c for c in COLS_LITE if c not in [ AutoEvalColumn.dummy.name, AutoEvalColumn.model.name, AutoEvalColumn.model_type_symbol.name, ] ], label="", elem_id="column-select", interactive=True, ) # with gr.Column(min_width=780): with gr.Row(): search_bar = gr.Textbox( placeholder="🔍 Search for a model and press ENTER...", show_label=False, elem_id="search-bar", ) filter_columns = gr.Radio( label="⏚ Filter model types", choices=["All", "🟢 Base", "🔶 Finetuned"], value="All", elem_id="filter-columns", ) df = pd.read_csv("data/code_eval_board.csv") leaderboard_df = gr.components.Dataframe( value=df[ [ AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name, ] + shown_columns.value ], headers=[ AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name, ] + shown_columns.value, datatype=TYPES, elem_id="leaderboard-table", interactive=False, ) hidden_leaderboard_df = gr.components.Dataframe( value=df, headers=COLS, datatype=["str" for _ in range(len(COLS))], visible=False, ) search_bar.submit( search_table, [hidden_leaderboard_df, leaderboard_df, search_bar], leaderboard_df, ) filter_columns.change( filter_items, [hidden_leaderboard_df, leaderboard_df, filter_columns], leaderboard_df, ) shown_columns.change( select_columns, [hidden_leaderboard_df, shown_columns], leaderboard_df, ) gr.Markdown( """ **Notes:** - The Huggingface team is working on their own implementation of this paper as a space, I'll be leaving this space up until that's available. - Some scores may not be entirely accurate according to the paper cited as I still work out the kinks and innacuracies of this implementation. - For any issues, questions, or comments either open a discussion in this space's community tab or message me directly to my discord: yeyito777. - Make sure to check the pinned discussion in this space's community tab for implementation details I'm not 100% about. """, elem_classes="markdown-text", ) with gr.TabItem("📝 About", id=2): gr.Markdown(ABOUT_TEXT, elem_classes="markdown-text") with gr.TabItem("🛠️ Submit models", id=3): gr.Markdown(SUBMISSION_TEXT) gr.Markdown( "## 📤 Submit a model here:", elem_classes="markdown-text" ) with gr.Column(): with gr.Row(): model_name = gr.Textbox(label="Model name") revision_name = gr.Textbox( label="revision", placeholder="main" ) with gr.Row(): precision = gr.Dropdown( choices=[ "float16", "bfloat16", "8bit", "4bit", ], label="Precision", multiselect=False, value="float16", interactive=True, ) model_type = gr.Dropdown( choices=["🟢 base", "🔶 instruction-tuned"], label="Model type", multiselect=False, value=None, interactive=True, ) submit_button = gr.Button("Submit Eval") submission_result = gr.Markdown() submit_button.click( add_new_eval, inputs=[model_name, revision_name, precision, model_type], outputs=[submission_result], ) gr.Markdown(SUBMISSION_TEXT_2) thread = Thread(target=worker_thread) thread.start() demo.launch(share=True) # Some worries: # 1. Am I testing things correctly in eval.py, following the template format? # 2. Am I choosing the correct splits in run.py? The higherarchy I use is: test > val > train # (As in: if test exists, I go with that, then validation, then default) # 3. I decided to go with winogrande_debiased instead of winogrande_l arbitrarily. # (Not sure which one open llm leaderboard uses, or what is the standard) # 4. I'm unsure why in eval.py we append the output at the end of the input. # 5. Currently I'm using huggyllama/llama-7b as ref_model, should I switch to llama2-7B? Maybe Mistral-7B?