docstr: A test that tests the simple usage of `DPOTrainer` using a bare model in full precision.
docstr_tok: ["A","test","that","tests","the","simple","usage","of","`","DPOTrainer","`","using","a","bare","model","in","full","precision","."]
code: def test_dpo_bare_model(
      self, model_id, loss_type, pre_compute_logits
  ):
      """
      A test that tests the simple usage of `DPOTrainer` using a bare model in full precision.
      """
      model = AutoModelForCausalLM.from_pretrained(model_id)
      tokenizer = AutoTokenizer.from_pretrained(model_id)
  
      with tempfile.TemporaryDirectory() as tmp_dir:
          training_args = TrainingArguments(
              output_dir=tmp_dir,
              per_device_train_batch_size=2,
              max_steps=2,
              remove_unused_columns=False,
              gradient_accumulation_steps=2,
              learning_rate=9e-1,
              evaluation_strategy="steps",
              fp16=True,
              logging_strategy="no",
              report_to="none",
          )
  
          # dpo train lora model
          trainer = DPOTrainer(
              model=model,
              ref_model=None,
              beta=0.1,
              args=training_args,
              tokenizer=tokenizer,
              train_dataset=self.dataset,
              eval_dataset=self.dataset,
              loss_type=loss_type,
              precompute_ref_log_probs=pre_compute_logits,
              max_length=self.max_length,
          )
  
          # train the model
          trainer.train()
  
          # save trained model or adapter
          trainer.save_model()
  
      release_memory(model, trainer)
  
imports: import gc
  import itertools
  import tempfile
  import unittest
  import torch
  from accelerate.utils.memory import release_memory
  from datasets import load_dataset
  from parameterized import parameterized
  from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments
  from trl import DPOTrainer, is_peft_available
  from ..testing_utils import require_bitsandbytes, require_peft, require_torch_gpu, require_torch_multi_gpu
  from .testing_constants import DPO_LOSS_TYPES, DPO_PRECOMPUTE_LOGITS, GRADIENT_CHECKPOINTING_KWARGS, MODELS_TO_TEST
label_desc: Use image node_id 3 for calling the DPOTrainerSlowTester obj's underlying member method code with example usage: obj.test_dpo_bare_model(model_id, loss_type, pre_compute_logits) without return types