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| 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'<a target="_blank" href="https://ai.facebook.com/blog/large-language-model-llama-meta-ai/" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model}</a>' | |
| if model_name == "HuggingFaceH4/stable-vicuna-13b-2904": | |
| link = "https://huggingface.co/" + "CarperAI/stable-vicuna-13b-delta" | |
| return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">stable-vicuna-13b</a>' | |
| if model_name == "HuggingFaceH4/llama-7b-ift-alpaca": | |
| link = "https://crfm.stanford.edu/2023/03/13/alpaca.html" | |
| return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">alpaca-13b</a>' | |
| # remove user from model name | |
| # model_name_show = ' '.join(model_name.split('/')[1:]) | |
| link = "https://huggingface.co/" + model_name | |
| return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>' | |
| 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; | |
| } | |
| """ | |