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 EVAL_REQUESTS_PATH = "eval-queue" QUEUE_REPO = "open-llm-leaderboard/requests" precisions = ("float16", "bfloat16", "8bit (LLM.int8)", "4bit (QLoRA / FP4)", "GPTQ") 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", 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()