如何评价 LLaMA 模型泄露？

import argparse
from flask import Flask, request, jsonify
from waitress import serve
from typing import Tuple
import os
import sys
import torch
import time
import json
from pathlib import Path
import torch.distributed as dist
from fairscale.nn.model_parallel.initialize import initialize_model_parallel
from llama import ModelArgs, Transformer, Tokenizer, LLaMA
app = Flask(__name__)
def setup_model_parallel() -> Tuple[int, int]:
    local_rank = int(os.environ.get("LOCAL_RANK", -1))
    world_size = int(os.environ.get("WORLD_SIZE", -1))
    print(f"local:{local_rank},world:{world_size}")
    dist.init_process_group("nccl")
    initialize_model_parallel(world_size)
    torch.cuda.set_device(local_rank)
    # seed must be the same in all processes
    torch.manual_seed(1)
    return local_rank, world_size
def load(ckpt_dir: str, tokenizer_path: str, local_rank: int, world_size: int) -> LLaMA:
    start_time = time.time()
    checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
    assert (
        world_size == len(checkpoints)
    ), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {world_size}"
    ckpt_path = checkpoints[local_rank]
    print("Loading")
    checkpoint = torch.load(ckpt_path, map_location="cpu")
    with open(Path(ckpt_dir) / "params.json", "r") as f:
        params = json.loads(f.read())
    model_args: ModelArgs = ModelArgs(max_seq_len=1024, max_batch_size=8, **params)
    tokenizer = Tokenizer(model_path=tokenizer_path)
    model_args.vocab_size = tokenizer.n_words
    torch.set_default_tensor_type(torch.cuda.HalfTensor)
    model = Transformer(model_args)
    torch.set_default_tensor_type(torch.FloatTensor)
    model.load_state_dict(checkpoint, strict=False)
    generator = LLaMA(model, tokenizer)
    print(f"Loaded in {time.time() - start_time:.2f} seconds")
    return generator
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--ckpt_dir")
    parser.add_argument("--tokenizer_path", type=str)
    parser.add_argument("--port", type=int,default=7860)
    parser.add_argument("--host", default="0.0.0.0")
    args = parser.parse_args()
    local_rank, world_size = setup_model_parallel()
    # if local_rank > 0:
    #     sys.stdout = open(os.devnull, 'w')
    generator = load(args.ckpt_dir, args.tokenizer_path, local_rank, world_size)
    print(f"LLaMA web-server at host {args.host}  port {args.port+local_rank}")
    #--------------------------------------------------------WHAT I ADDED--------------------------------------------------------------
    # import traceback
    # @app.errorhandler(Exception)
    # def haddle_error(e):
    #     return "Internal Server Error", 500
    @app.route("/infer", methods=["POST"])
    def generate():
        input = request.json