src/envs.py

import os

from huggingface_hub import HfApi

# clone / pull the lmeh eval data
H4_TOKEN = os.environ.get("H4_TOKEN", None)

REPO_ID = os.getenv("REPO_ID", "HuggingFaceH4/open_llm_leaderboard")
QUEUE_REPO = os.getenv("QUEUE_REPO", "open-llm-leaderboard/requests")
DYNAMIC_INFO_REPO = "open-llm-leaderboard/dynamic_model_information"
RESULTS_REPO = os.getenv("RESULTS_REPO", "open-llm-leaderboard/results")

PRIVATE_QUEUE_REPO = QUEUE_REPO
PRIVATE_RESULTS_REPO = RESULTS_REPO
#PRIVATE_QUEUE_REPO = "open-llm-leaderboard/private-requests"
#PRIVATE_RESULTS_REPO = "open-llm-leaderboard/private-results"

IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True))

CACHE_PATH=os.getenv("HF_HOME", ".")

EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue")
EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results")
DYNAMIC_INFO_PATH = os.path.join(CACHE_PATH, "dynamic-info")
DYNAMIC_INFO_FILE_PATH = os.path.join(DYNAMIC_INFO_PATH, "model_infos.json")

EVAL_REQUESTS_PATH_PRIVATE = "eval-queue-private"
EVAL_RESULTS_PATH_PRIVATE = "eval-results-private"

PATH_TO_COLLECTION = os.getenv("PATH_TO_COLLECTION", "open-llm-leaderboard/llm-leaderboard-best-models-652d6c7965a4619fb5c27a03")

# Rate limit variables
RATE_LIMIT_PERIOD = int(os.getenv("RATE_LIMIT_PERIOD", 7))
RATE_LIMIT_QUOTA = int(os.getenv("RATE_LIMIT_QUOTA", 5))
HAS_HIGHER_RATE_LIMIT = os.environ.get("HAS_HIGHER_RATE_LIMIT", "TheBloke").split(',')

API = HfApi(token=H4_TOKEN)