Fine-tuning/result.txt

(base) [root@localhost ~]# docker exec finetune-trainer bash -c 'sed -n "1400,1450p" /opt/conda/lib/python3.10/site-packages/trl/trainer/dpo_trainer.py'
            warnings.warn(
                "compute_loss is only implemented for DPODataCollatorWithPadding, and you passed a datacollator that is different than "
                "DPODataCollatorWithPadding - you might see unexpected behavior. Alternatively, you can implement your own prediction_step method if you are using a custom data collator"
            )

        compute_loss_context_manager = torch.cuda.amp.autocast if self._peft_has_been_casted_to_bf16 else nullcontext

        with compute_loss_context_manager():
            loss, metrics = self.get_batch_loss_metrics(model, inputs, train_eval="train")

        # Make sure to move the loss to the device the original accumulating loss is at back in the `Trainer` class:
        loss = loss.to(self.args.device)
        # force log the metrics
        self.store_metrics(metrics, train_eval="train")

        if return_outputs:
            return (loss, metrics)
        return loss

    def get_batch_samples(self, model, batch: Dict[str, torch.LongTensor]) -> Tuple[str, str]:
        """Generate samples from the model and reference model for the given batch of inputs."""

        # If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with
        # the torch cuda amp context manager as some hidden states are silently casted to full precision.
        generate_context_manager = nullcontext if not self._peft_has_been_casted_to_bf16 else torch.cuda.amp.autocast

        with generate_context_manager():
            policy_output = model.generate(
                input_ids=batch["prompt_input_ids"],
                attention_mask=batch["prompt_attention_mask"],
                max_length=self.max_length,
                do_sample=True,
                pad_token_id=self.tokenizer.pad_token_id,
            )

            # if reference_output in batch use that otherwise use the reference model
            if "reference_output" in batch:
                reference_output = batch["reference_output"]
            else:
                if self.ref_model is None:
                    with self.null_ref_context():
                        reference_output = self.model.generate(
                            input_ids=batch["prompt_input_ids"],
                            attention_mask=batch["prompt_attention_mask"],
                            max_length=self.max_length,
                            do_sample=True,
                            pad_token_id=self.tokenizer.pad_token_id,
                        )
                else:
                    reference_output = self.ref_model.generate(
                        input_ids=batch["prompt_input_ids"],