| import os | |
| from huggingface_hub import HfApi | |
| # clone / pull the lmeh eval data | |
| H4_TOKEN = os.environ.get("H4_TOKEN", None) | |
| REPO_ID = "HuggingFaceH4/open_llm_leaderboard" | |
| QUEUE_REPO = "open-llm-leaderboard/requests" | |
| RESULTS_REPO = "open-llm-leaderboard/results" | |
| 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)) | |
| EVAL_REQUESTS_PATH = "eval-queue" | |
| EVAL_RESULTS_PATH = "eval-results" | |
| EVAL_REQUESTS_PATH_PRIVATE = "eval-queue-private" | |
| EVAL_RESULTS_PATH_PRIVATE = "eval-results-private" | |
| PATH_TO_COLLECTION = "open-llm-leaderboard/llm-leaderboard-best-models-652d6c7965a4619fb5c27a03" | |
| # Rate limit variables | |
| RATE_LIMIT_PERIOD = 7 | |
| RATE_LIMIT_QUOTA = 5 | |
| HAS_HIGHER_RATE_LIMIT = ["TheBloke"] | |
| API = HfApi(token=H4_TOKEN) | |