import os import shutil import numpy as np import gradio as gr from huggingface_hub import Repository, HfApi from transformers import AutoConfig, AutoModel import json from apscheduler.schedulers.background import BackgroundScheduler import pandas as pd import datetime import glob from dataclasses import dataclass from typing import List, Tuple, Dict # clone / pull the lmeh eval data H4_TOKEN = os.environ.get("H4_TOKEN", None) LMEH_REPO = "HuggingFaceH4/lmeh_evaluations" METRICS = ["acc_norm", "acc_norm", "acc_norm", "mc2"] BENCHMARKS = ["arc_challenge", "hellaswag", "hendrycks", "truthfulqa_mc"] BENCH_TO_NAME = { "arc_challenge": "ARC (25-shot) ⬆️", "hellaswag": "HellaSwag (10-shot) ⬆️", "hendrycks": "MMLU (5-shot) ⬆️", "truthfulqa_mc": "TruthfulQA (0-shot) ⬆️", } def make_clickable_model(model_name): LLAMAS = [ "huggingface/llama-7b", "huggingface/llama-13b", "huggingface/llama-30b", "huggingface/llama-65b", ] if model_name in LLAMAS: model = model_name.split("/")[1] return f'{model}' if model_name == "HuggingFaceH4/stable-vicuna-13b-2904": link = "https://huggingface.co./" + "CarperAI/stable-vicuna-13b-delta" return f'stable-vicuna-13b' if model_name == "HuggingFaceH4/llama-7b-ift-alpaca": link = "https://crfm.stanford.edu/2023/03/13/alpaca.html" return f'alpaca-13b' # remove user from model name # model_name_show = ' '.join(model_name.split('/')[1:]) link = "https://huggingface.co./" + model_name return f'{model_name}' @dataclass class EvalResult: eval_name: str org: str model: str revision: str is_8bit: bool results: dict def to_dict(self): if self.org is not None: base_model = f"{self.org}/{self.model}" else: base_model = f"{self.model}" data_dict = {} data_dict["eval_name"] = self.eval_name data_dict["8bit"] = self.is_8bit data_dict["Model"] = make_clickable_model(base_model) # dummy column to implement search bar (hidden by custom CSS) data_dict["model_name_for_query"] = base_model data_dict["Revision"] = self.revision data_dict["Average ⬆️"] = round( sum([v for k, v in self.results.items()]) / 4.0, 1 ) # data_dict["# params"] = get_n_params(base_model) for benchmark in BENCHMARKS: if not benchmark in self.results.keys(): self.results[benchmark] = None for k, v in BENCH_TO_NAME.items(): data_dict[v] = self.results[k] return data_dict def parse_eval_result(json_filepath: str) -> Tuple[str, dict]: with open(json_filepath) as fp: data = json.load(fp) path_split = json_filepath.split("/") org = None model = path_split[-4] is_8bit = path_split[-2] == "8bit" revision = path_split[-3] if len(path_split) == 7: # handles gpt2 type models that don't have an org result_key = f"{path_split[-4]}_{path_split[-3]}_{path_split[-2]}" else: result_key = ( f"{path_split[-5]}_{path_split[-4]}_{path_split[-3]}_{path_split[-2]}" ) org = path_split[-5] eval_result = None for benchmark, metric in zip(BENCHMARKS, METRICS): if benchmark in json_filepath: accs = np.array([v[metric] for k, v in data["results"].items()]) mean_acc = round(np.mean(accs) * 100.0, 1) eval_result = EvalResult( result_key, org, model, revision, is_8bit, {benchmark: mean_acc} ) return result_key, eval_result def get_eval_results(is_public) -> List[EvalResult]: json_filepaths = glob.glob( "evals/eval_results/public/**/16bit/*.json", recursive=True ) if not is_public: json_filepaths += glob.glob( "evals/eval_results/private/**/*.json", recursive=True ) json_filepaths += glob.glob( "evals/eval_results/private/**/*.json", recursive=True ) json_filepaths += glob.glob( "evals/eval_results/public/**/8bit/*.json", recursive=True ) # include the 8bit evals of public models eval_results = {} for json_filepath in json_filepaths: result_key, eval_result = parse_eval_result(json_filepath) if result_key in eval_results.keys(): eval_results[result_key].results.update(eval_result.results) else: eval_results[result_key] = eval_result eval_results = [v for k, v in eval_results.items()] return eval_results def get_eval_results_dicts(is_public=True) -> List[Dict]: eval_results = get_eval_results(is_public) return [e.to_dict() for e in eval_results] eval_results_dict = get_eval_results_dicts() # print(eval_results_dict)