backend / utils.py
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Add GPT-4 & human eval tab
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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>'
@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]