docstr: Additional arguments for PPO training process.
  
  Args:
      reward_model (Union[str, "transformers.pipeline"]): Reward model to use for creating PPO rewards for training.
      length_sampler_kwargs (dict): Arguments for the length sampler.
          min_value: Minimum length for the generated samples.
          max_value: Maximum length for the generated samples.
      generation_kwargs (dict): Arguments for the generation process.
          min_length: Minimum length for the generated samples.
          max_length: Maximum length for the generated samples.
          num_beams: Number of beams to use for the generation process.
          num_return_sequences: Number of sequences to generate.
          temperature: Temperature for the generation process.
          top_k: Top k for the generation process.
          top_p: Top p for the generation process.
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code: def __init__(
      self,
      config: "PPOConfig",
      reward_model: Union[str, "transformers.pipeline"],
      length_sampler_kwargs: dict,
      generation_kwargs: dict,
  ) -> None:
      """
      Additional arguments for PPO training process.
  
      Args:
          reward_model (Union[str, "transformers.pipeline"]): Reward model to use for creating PPO rewards for training.
          length_sampler_kwargs (dict): Arguments for the length sampler.
              min_value: Minimum length for the generated samples.
              max_value: Maximum length for the generated samples.
          generation_kwargs (dict): Arguments for the generation process.
              min_length: Minimum length for the generated samples.
              max_length: Maximum length for the generated samples.
              num_beams: Number of beams to use for the generation process.
              num_return_sequences: Number of sequences to generate.
              temperature: Temperature for the generation process.
              top_k: Top k for the generation process.
              top_p: Top p for the generation process.
      """
      if isinstance(reward_model, str):
          from transformers import pipeline
  
          reward_model = pipeline(
              "text-classifcation", model=reward_model
          )
      self.config = config
      self.reward_model = reward_model
      self.length_sampler_kwargs = length_sampler_kwargs
      self.generation_kwargs = generation_kwargs
  
file_path: argilla/src/argilla/client/feedback/training/frameworks/trl.py
imports: import logging
  from typing import TYPE_CHECKING, Dict, List, Optional, Union
  from argilla.client.feedback.training.base import ArgillaTrainerSkeleton
  from argilla.client.feedback.training.schemas.base import TrainingTaskForDPO, TrainingTaskForPPO, TrainingTaskForRM, TrainingTaskForSFT
  from argilla.training.utils import filter_allowed_args
  from argilla.utils.dependency import require_dependencies, requires_dependencies
label_desc: Use image node_id 1 to create a new PPOArgs object  with example: obj = PPOArgs(config, reward_model, length_sampler_kwargs, generation_kwargs)