Upload logos_reward_ft_job.py with huggingface_hub

logos_reward_ft_job.py

@@ -42,8 +42,8 @@ from datasets import Dataset
 from transformers import (
     WhisperProcessor,
     WhisperForConditionalGeneration,
-    Seq2SeqTrainer,
-    Seq2SeqTrainingArguments,
+    Trainer,
+    TrainingArguments,
 )
 from peft import LoraConfig, get_peft_model, TaskType
 from huggingface_hub import login
@@ -277,7 +277,7 @@ model = get_peft_model(model, lora_cfg)
 log.info(str(model.print_trainable_parameters()))
 
 # ── Reward-weighted Trainer ───────────────────────────────────────────────────
-class RewardWeightedTrainer(Seq2SeqTrainer):
+class RewardWeightedTrainer(Trainer):
     def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
         reward_weights = inputs.pop("reward_weight").to(model.device)
         labels         = inputs["labels"]
@@ -314,7 +314,7 @@ class WhisperRewardCollator:
 
 collator = WhisperRewardCollator()
 
-training_args = Seq2SeqTrainingArguments(
+training_args = TrainingArguments(
     output_dir                  = OUTPUT_DIR,
     per_device_train_batch_size = BATCH_SIZE,
     per_device_eval_batch_size  = BATCH_SIZE,
@@ -331,7 +331,6 @@ training_args = Seq2SeqTrainingArguments(
     load_best_model_at_end      = True,
     metric_for_best_model       = "eval_loss",
     greater_is_better           = False,
-    predict_with_generate       = False,
     push_to_hub                 = False,
     remove_unused_columns       = False,
 )