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