code: def get_specific(model_name, tokenizer, model, args):
      data_collator = DataCollatorForSeq2Seq(
          tokenizer,
          pad_to_multiple_of=8,
          return_tensors="pt",
          padding=True,
      )
      prompter = Prompter(args.prompt_template_name)
      coll_fn = COLL_FN_DICT[model_name]()
      if model_name == "llama":
          model.config.pad_token_id = tokenizer.pad_token_id = 0
          model.config.bos_token_id = tokenizer.bos_token_id = 1
          model.config.eos_token_id = tokenizer.eos_token_id = 2
          tokenizer.padding_side = "left"
      elif model_name == "moss":
          model.config.pad_token_id = (
              tokenizer.pad_token_id
          ) = tokenizer.eos_token_id
      elif model_name == "cpm-bee":
          data_collator = DataCollatorForCPMBEE(
              tokenizer, args.cutoff_len
          )
      return coll_fn, data_collator, prompter
  
file_path: DeepKE/example/llm/InstructKGC/src/finetune.py
imports: import os
  import sys
  import logging
  import argparse
  from datasets import Dataset
  import torch
  from transformers import HfArgumentParser, DataCollatorForSeq2Seq, GenerationConfig, BitsAndBytesConfig
  from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training, set_peft_model_state_dict
  from peft.tuners.lora import LoraLayer
  from utils.general_utils import LORA_TARGET_MODULES_DICT, seed_torch, get_model_tokenizer, get_model_name, get_format_time
  from utils.prompter import Prompter
  from utils.args import DataArguments, ModelArguments, TrainingArguments, GenerationArguments
  from datamodule import COLL_FN_DICT, DataCollatorForCPMBEE
label_desc: Use image node_id 1 for calling a global function with example usage: get_specific(model_name, tokenizer, model, args) and returns: coll_fn,  data_collator,  prompter