Spaces:
Paused
Paused
File size: 1,076 Bytes
2a5f9fb df66f6e 2a5f9fb 1ffc326 049923d f982b8e a08a2d9 08ae6c5 22e8b00 08ae6c5 2c38fe4 18abd06 1ffc326 2a5f9fb b128812 9833cdb c500f60 2a5f9fb 1ffc326 4ff9eef 395eff6 9833cdb 395eff6 1ffc326 2a5f9fb efeee6d 08ae6c5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 |
import os
from huggingface_hub import HfApi
# Info to change for your repository
# ----------------------------------
TOKEN = os.environ.get("HF_TOKEN") # A read/write token for your org
OWNER = "hebrew-llm-leaderboard" # Change to your org - don't forget to create a results and request file
# For harness evaluations
DEVICE = "cpu" # "cuda:0" if you add compute, for harness evaluations
LIMIT = None # !!!! Should be None for actual evaluations!!!
# For lighteval evaluations
ACCELERATOR = "gpu"
REGION = "us-east-1"
VENDOR = "aws"
# ----------------------------------
REPO_ID = f"{OWNER}/backend"
QUEUE_REPO = f"{OWNER}/requests"
RESULTS_REPO = f"{OWNER}/private-results"
# If you setup a cache later, just change HF_HOME
CACHE_PATH=os.getenv("HF_HOME", ".")
# Local caches
EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue")
EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results")
EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk")
EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-results-bk")
API = HfApi(token=TOKEN)
|