import glob import json from dataclasses import dataclass from typing import Dict, List, Tuple import numpy as np # clone / pull the lmeh eval data 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) 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 ) 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] get_window_url_params = """ function(url_params) { const params = new URLSearchParams(window.location.search); url_params = Object.fromEntries(params); return url_params; } """