code: def merge_and_push(args):
      # Reload model in FP16 and merge it with LoRA weights
      base_model = AutoModelForCausalLM.from_pretrained(
          args.model_name,
          low_cpu_mem_usage=True,
          return_dict=True,
          torch_dtype=torch.float16,
          device_map="auto",
      )
      model = PeftModel.from_pretrained(base_model, args.new_model_name)
      model = model.merge_and_unload()
  
      # Reload the new tokenizer
      tokenizer = AutoTokenizer.from_pretrained(
          args.model_name, trust_remote_code=True
      )
      tokenizer.pad_token = tokenizer.eos_token
      tokenizer.padding_side = "right"
  
      # Publish the new model to Hugging Face Hub
      model.push_to_hub(args.new_model_name, use_temp_dir=False)
      tokenizer.push_to_hub(args.new_model_name, use_temp_dir=False)
  
file_path: dstack/src/dstack/api/_public/huggingface/finetuning/sft/train.py
imports: from dataclasses import dataclass, field
  from typing import Optional, Union
  import torch
  from datasets import load_dataset
  from peft import LoraConfig, PeftModel
  from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments
  from trl import SFTTrainer
label_desc: Use image node_id 3 for calling a global function with example usage: merge_and_push(args) without return types