( ref_model: typing.Union[transformers.modeling_utils.PreTrainedModel, torch.nn.modules.module.Module] accelerator: typing.Optional[accelerate.accelerator.Accelerator] )
A TrainerCallback that displays the progress of training or evaluation using Rich.
( judge: BasePairwiseJudge trainer: Trainer generation_config: typing.Optional[transformers.generation.configuration_utils.GenerationConfig] = None num_prompts: typing.Optional[int] = None shuffle_order: bool = True use_soft_judge: bool = False )
Parameters
BasePairwiseJudge) —
The judge to use for comparing completions. Trainer) —
Trainer to which the callback will be attached. The trainer’s evaluation dataset must include a "prompt"
column containing the prompts for generating completions. If the Trainer has a reference model (via the
ref_model attribute), it will use this reference model for generating the reference completions;
otherwise, it defaults to using the initial model. GenerationConfig, optional) —
The generation config to use for generating completions. int or None, optional, defaults to None) —
The number of prompts to generate completions for. If not provided, defaults to the number of examples
in the evaluation dataset. bool, optional, defaults to True) —
Whether to shuffle the order of the completions before judging. bool, optional, defaults to False) —
Whether to use a soft judge that returns a win probability between 0 and 1 for the first completion vs the
second. A TrainerCallback that computes the win rate of a model based on a reference.
It generates completions using prompts from the evaluation dataset and compares the trained model’s outputs against
a reference. The reference is either the initial version of the model (before training) or the reference model, if
available in the trainer. During each evaluation step, a judge determines how often the trained model’s completions
win against the reference using a judge. The win rate is then logged in the trainer’s logs under the key
"eval_win_rate".
( trainer: Trainer generation_config: typing.Optional[transformers.generation.configuration_utils.GenerationConfig] = None num_prompts: typing.Optional[int] = None freq: typing.Optional[int] = None )
Parameters
Trainer) —
Trainer to which the callback will be attached. The trainer’s evaluation dataset must include a "prompt"
column containing the prompts for generating completions. GenerationConfig, optional) —
The generation config to use for generating completions. int or None, optional) —
The number of prompts to generate completions for. If not provided, defaults to the number of examples in the evaluation dataset. int or None, optional) —
The frequency at which to log completions. If not provided, defaults to the trainer’s eval_steps. A TrainerCallback that logs completions to Weights & Biases.
( merge_config: typing.Optional[ForwardRef('MergeConfig')] = None merge_at_every_checkpoint: bool = False push_to_hub: bool = False )
Parameters
MergeConfig, optional, defaults to None) —
Configuration used for the merging process. If not provided, the default MergeConfig is used. bool, optional, defaults to False) —
Whether to merge the model at every checkpoint. bool, optional, defaults to False) —
Whether to push the merged model to the Hub after merging. A TrainerCallback that merges the policy model (the model being trained) with another model based on a merge configuration.