| @@ -209,8 +209,8 @@ def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: |
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labels[: len(contxt_id)] = -100 |
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if self.accelerator.is_main_process and doc_id % 100 == 0: |
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-eval_logger.info(f"Prompt for doc ID {doc_id}:\n\n{formatted_contexts[0]}\n") |
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-eval_logger.info(f"Prompt and continuation for doc ID {doc_id}:\n\n{formatted_continuation[0]}\n") |
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+eval_logger.debug(f"Prompt for doc ID {doc_id}:\n\n{formatted_contexts[0]}\n") |
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+eval_logger.debug(f"Prompt and continuation for doc ID {doc_id}:\n\n{formatted_continuation[0]}\n") |
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with torch.inference_mode(): |
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outputs = self.model(**model_inputs, labels=labels) |
| @@ -293,7 +293,7 @@ def _collate(x): |
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text = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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if self.accelerator.is_main_process and doc_id[0] % 100 == 0: |
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-eval_logger.info(f"Prompt for doc ID {doc_id[0]}:\n\n{text}\n") |
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+eval_logger.debug(f"Prompt for doc ID {doc_id[0]}:\n\n{text}\n") |
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inputs = self._image_processor(images=visuals, text=text, return_tensors="pt").to(self._device, self.model.dtype) |
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| @@ -329,7 +329,7 @@ def _collate(x): |
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text_outputs = text_outputs.split("ASSISTANT:")[-1].strip() |
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if self.accelerator.is_main_process and doc_id[0] % 100 == 0: |
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-eval_logger.info(f"Generated text for doc ID {doc_id[0]}:\n\n{text_outputs}\n") |
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+eval_logger.debug(f"Generated text for doc ID {doc_id[0]}:\n\n{text_outputs}\n") |
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res.append(text_outputs) |
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self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs) |