Upload trainer.py with huggingface_hub

trainer.py

@@ -1,4 +1,5 @@
 
+
 # coding=utf-8
 # Copyright 2020-present the HuggingFace Inc. team.
 #
@@ -384,7 +385,7 @@ class Trainer:
                 f"The model you have picked ({model.__class__.__name__}) cannot be used as is for training: it only "
                 "computes hidden states and does not accept any labels. You should choose a model with a head "
                 "suitable for your task like any of the `AutoModelForXxx` listed at "
-                "https://huggingface.co/docs/transformers/model_doc/auto"
+                "https://hf-mirror.com/docs/transformers/model_doc/auto"
             )
 
         if hasattr(model, "is_parallelizable") and model.is_parallelizable and model.model_parallel:
@@ -416,7 +417,7 @@ class Trainer:
         if _is_quantized_and_base_model and not _is_peft_model(model):
             raise ValueError(
                 "You cannot perform fine-tuning on purely quantized models. Please attach trainable adapters on top of"
-                " the quantized model to correctly perform fine-tuning. Please see: https://huggingface.co/docs/transformers/peft"
+                " the quantized model to correctly perform fine-tuning. Please see: https://hf-mirror.com/docs/transformers/peft"
                 " for more details"
             )
         elif _is_quantized_and_base_model and not getattr(model, "_is_quantized_training_enabled", False):
@@ -2805,14 +2806,14 @@ class Trainer:
         #######################################################
         import json
         for i in range(len(data_info_temp)):
-            # data_info_temp[i]['loss'] = float(loss[0][i]) 
+            data_info_temp[i]['loss'] = float(loss[0][i]) 
             data_info_temp[i]['yes_target_logprob'] = yes_target_logprob
             data_info_temp[i]['logits_shape'] = outputs.logits.shape
 
         from datetime import datetime 
         current_time = datetime.now().strftime('%Y_%m_%d')
 
-        file_path = '/data/zbz5349/ICLR_2024/ACL_2025/LLaVA_Fliter/inference_demo/cherry_AskLLM_infer_result_' + current_time + '.jsonl'
+        file_path = '/data/zbz5349/ICLR_2024/ACL_2025/LLaVA_Fliter/inference_demo/math_cherry_AskLLM_infer_result_' + current_time + '.jsonl'
         with open(file_path, 'a', encoding='utf-8') as file:
             # json.dump(data_info_temp[0], file, ensure_ascii=False, indent=4)
             for content in data_info_temp:
@@ -4088,3 +4089,4 @@ class Trainer:
         ds_plugin.hf_ds_config.trainer_config_process(self.args, auto_find_batch_size)
 
 
+