import os import shutil import numpy as np import gradio as gr from huggingface_hub import Repository, HfApi from transformers import AutoConfig import json from apscheduler.schedulers.background import BackgroundScheduler import pandas as pd import datetime from utils import get_eval_results_dicts, make_clickable_model # clone / pull the lmeh eval data H4_TOKEN = os.environ.get("H4_TOKEN", None) LMEH_REPO = "HuggingFaceH4/lmeh_evaluations" IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", None)) repo=None if H4_TOKEN: print("pulling repo") # try: # shutil.rmtree("./evals/") # except: # pass repo = Repository( local_dir="./evals/", clone_from=LMEH_REPO, use_auth_token=H4_TOKEN, repo_type="dataset" ) repo.git_pull() # parse the results BENCHMARKS = ["arc_challenge", "hellaswag", "hendrycks", "truthfulqa_mc"] METRICS = ["acc_norm", "acc_norm", "acc_norm", "mc2"] def load_results(model, benchmark, metric): file_path = os.path.join("evals", model, f"{model}-eval_{benchmark}.json") if not os.path.exists(file_path): return 0.0, None with open(file_path) as fp: data = json.load(fp) accs = np.array([v[metric] for k, v in data["results"].items()]) mean_acc = np.mean(accs) return mean_acc, data["config"]["model_args"] COLS = ["Model", "Revision", "Average ⬆️", "ARC (25-shot) ⬆️", "HellaSwag (10-shot) ⬆️", "MMLU (5-shot) ⬆️", "TruthfulQA (0-shot) ⬆️"] TYPES = ["markdown","str", "number", "number", "number", "number", "number", ] if not IS_PUBLIC: COLS.insert(2, "8bit") TYPES.insert(2, "bool") EVAL_COLS = ["model", "revision", "private", "8bit_eval", "is_delta_weight", "status"] EVAL_TYPES = ["markdown","str", "bool", "bool", "bool", "str"] def get_leaderboard(): if repo: print("pulling changes") repo.git_pull() all_data = get_eval_results_dicts(IS_PUBLIC) if not IS_PUBLIC: gpt4_values = { "Model":f'gpt4', "Revision":"tech report", "8bit":None, "Average ⬆️":84.3, "ARC (25-shot) ⬆️":96.3, "HellaSwag (10-shot) ⬆️":95.3, "MMLU (5-shot) ⬆️":86.4, "TruthfulQA (0-shot) ⬆️":59.0, } all_data.append(gpt4_values) gpt35_values = { "Model":f'gpt3.5', "Revision":"tech report", "8bit":None, "Average ⬆️":71.9, "ARC (25-shot) ⬆️":85.2, "HellaSwag (10-shot) ⬆️":85.5, "MMLU (5-shot) ⬆️":70.0, "TruthfulQA (0-shot) ⬆️":47.0, } all_data.append(gpt35_values) dataframe = pd.DataFrame.from_records(all_data) dataframe = dataframe.sort_values(by=['Average ⬆️'], ascending=False) print(dataframe) dataframe = dataframe[COLS] return dataframe def get_eval_table(): if repo: print("pulling changes for eval") repo.git_pull() entries = [entry for entry in os.listdir("evals/eval_requests") if not entry.startswith('.')] all_evals = [] for entry in entries: print(entry) if ".json"in entry: file_path = os.path.join("evals/eval_requests", entry) with open(file_path) as fp: data = json.load(fp) data["# params"] = "unknown" data["model"] = make_clickable_model(data["model"]) data["revision"] = data.get("revision", "main") all_evals.append(data) else: # this is a folder sub_entries = [e for e in os.listdir(f"evals/eval_requests/{entry}") if not e.startswith('.')] for sub_entry in sub_entries: file_path = os.path.join("evals/eval_requests", entry, sub_entry) with open(file_path) as fp: data = json.load(fp) #data["# params"] = get_n_params(data["model"]) data["model"] = make_clickable_model(data["model"]) all_evals.append(data) dataframe = pd.DataFrame.from_records(all_evals) return dataframe[EVAL_COLS] leaderboard = get_leaderboard() eval_queue = get_eval_table() def is_model_on_hub(model_name, revision) -> bool: try: config = AutoConfig.from_pretrained(model_name, revision=revision) return True except Exception as e: print("Could not get the model config from the hub") print(e) return False def add_new_eval(model:str, base_model : str, revision:str, is_8_bit_eval: bool, private:bool, is_delta_weight:bool): # check the model actually exists before adding the eval if revision == "": revision = "main" if is_delta_weight and not is_model_on_hub(base_model, revision): print(base_model, "base model not found on hub") return if not is_model_on_hub(model, revision): print(model, "not found on hub") return print("adding new eval") eval_entry = { "model" : model, "base_model" : base_model, "revision" : revision, "private" : private, "8bit_eval" : is_8_bit_eval, "is_delta_weight" : is_delta_weight, "status" : "PENDING" } user_name = "" model_path = model if "/" in model: user_name = model.split("/")[0] model_path = model.split("/")[1] OUT_DIR=f"eval_requests/{user_name}" os.makedirs(OUT_DIR, exist_ok=True) out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{is_8_bit_eval}_{is_delta_weight}.json" with open(out_path, "w") as f: f.write(json.dumps(eval_entry)) LMEH_REPO = "HuggingFaceH4/lmeh_evaluations" api = HfApi() api.upload_file( path_or_fileobj=out_path, path_in_repo=out_path, repo_id=LMEH_REPO, token=H4_TOKEN, repo_type="dataset", ) def refresh(): return get_leaderboard(), get_eval_table() block = gr.Blocks() with block: with gr.Row(): gr.Markdown(f""" # 🤗 Open LLM Leaderboard With the plethora of large language models (LLMs) and chatbots being released week upon week, often with grandiose claims of their performance, it can be hard to filter out the genuine progress that is being made by the open-source community and which model is the current state of the art. The 🤗 Open LLM Leaderboard aims to track, rank and evaluate LLMs and chatbots as they are released. We evaluate models on 4 key benchmarks from the Eleuther AI Language Model Evaluation Harness , a unified framework to test generative language models on a large number of different evaluation tasks. A key advantage of this leaderboard is that anyone from the community can submit a model for automated evaluation on the 🤗 GPU cluster, as long as it is a 🤗 Transformers model with weights on the Hub. We also support evaluation of models with delta-weights for non-commercial licensed models, such as LLaMa. Evaluation is performed against 4 popular benchmarks: - AI2 Reasoning Challenge (25-shot) - a set of grade-school science questions. - HellaSwag (10-shot) - a test of commonsense inference, which is easy for humans (~95%) but challenging for SOTA models. - MMLU (5-shot) - a test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. - TruthfulQA (0-shot) - a benchmark to measure whether a language model is truthful in generating answers to questions. We chose these benchmarks as they test a variety of reasoning and general knowledge across a wide variety of fields in 0-shot and few-shot settings. """) with gr.Row(): leaderboard_table = gr.components.Dataframe(value=leaderboard, headers=COLS, datatype=TYPES, max_rows=5) with gr.Row(): gr.Markdown(f""" # Evaluation Queue for the 🤗 Open LLM Leaderboard, these models will be automatically evaluated on the 🤗 cluster """) with gr.Accordion("Evaluation Queue", open=False): with gr.Row(): eval_table = gr.components.Dataframe(value=eval_queue, headers=EVAL_COLS, datatype=EVAL_TYPES, max_rows=5) with gr.Row(): refresh_button = gr.Button("Refresh") refresh_button.click(refresh, inputs=[], outputs=[leaderboard_table, eval_table]) with gr.Accordion("Submit a new model for evaluation"): with gr.Row(): with gr.Column(): model_name_textbox = gr.Textbox(label="Model name") revision_name_textbox = gr.Textbox(label="revision", placeholder="main") with gr.Column(): is_8bit_toggle = gr.Checkbox(False, label="8 bit eval", visible=not IS_PUBLIC) private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC) is_delta_weight = gr.Checkbox(False, label="Delta weights") base_model_name_textbox = gr.Textbox(label="base model (for delta)") with gr.Row(): submit_button = gr.Button("Submit Eval") submit_button.click(add_new_eval, [model_name_textbox, base_model_name_textbox, revision_name_textbox, is_8bit_toggle, private, is_delta_weight]) block.load(refresh, inputs=[], outputs=[leaderboard_table, eval_table]) block.launch()