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import json
import os
import pprint
import re
from datetime import datetime, timezone
import click
from colorama import Fore
from huggingface_hub import HfApi, snapshot_download
from src.envs import TOKEN
EVAL_REQUESTS_PATH = "eval-queue"
QUEUE_REPO = "open-llm-leaderboard/requests"
precisions = ("float16", "bfloat16", "8bit (LLM.int8)", "4bit (QLoRA / FP4)", "GPTQ", "float32")
model_types = ("pretrained", "fine-tuned", "RL-tuned", "instruction-tuned")
weight_types = ("Original", "Delta", "Adapter")
def get_model_size(model_info, precision: str):
size_pattern = size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
try:
model_size = round(model_info.safetensors["total"] / 1e9, 3)
except (AttributeError, TypeError):
try:
size_match = re.search(size_pattern, model_info.modelId.lower())
model_size = size_match.group(0)
model_size = round(float(model_size[:-1]) if model_size[-1] == "b" else float(model_size[:-1]) / 1e3, 3)
except AttributeError:
return 0 # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
model_size = size_factor * model_size
return model_size
def main():
api = HfApi()
current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
snapshot_download(repo_id=QUEUE_REPO, revision="main", token=TOKEN, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset")
model_name = click.prompt("Enter model name")
revision = click.prompt("Enter revision", default="main")
precision = click.prompt("Enter precision", default="float16", type=click.Choice(precisions))
model_type = click.prompt("Enter model type", type=click.Choice(model_types))
weight_type = click.prompt("Enter weight type", default="Original", type=click.Choice(weight_types))
base_model = click.prompt("Enter base model", default="")
status = click.prompt("Enter status", default="FINISHED")
try:
model_info = api.model_info(repo_id=model_name, revision=revision)
except Exception as e:
print(f"{Fore.RED}Could not find model info for {model_name} on the Hub\n{e}{Fore.RESET}")
return 1
model_size = get_model_size(model_info=model_info, precision=precision)
try:
license = model_info.cardData["license"]
except Exception:
license = "?"
eval_entry = {
"model": model_name,
"base_model": base_model,
"revision": revision,
"private": False,
"precision": precision,
"weight_type": weight_type,
"status": status,
"submitted_time": current_time,
"model_type": model_type,
"likes": model_info.likes,
"params": model_size,
"license": license,
}
user_name = ""
model_path = model_name
if "/" in model_name:
user_name = model_name.split("/")[0]
model_path = model_name.split("/")[1]
pprint.pprint(eval_entry)
if click.confirm("Do you want to continue? This request file will be pushed to the hub"):
click.echo("continuing...")
out_dir = f"{EVAL_REQUESTS_PATH}/{user_name}"
os.makedirs(out_dir, exist_ok=True)
out_path = f"{out_dir}/{model_path}_eval_request_{False}_{precision}_{weight_type}.json"
with open(out_path, "w") as f:
f.write(json.dumps(eval_entry))
api.upload_file(
path_or_fileobj=out_path,
path_in_repo=out_path.split(f"{EVAL_REQUESTS_PATH}/")[1],
repo_id=QUEUE_REPO,
repo_type="dataset",
commit_message=f"Add {model_name} to eval queue",
)
else:
click.echo("aborting...")
if __name__ == "__main__":
main()
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