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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 QUEUE_REPO, EVAL_REQUESTS_PATH | |
| 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() | |