Delete start_training.py

start_training.py

@@ -1,56 +0,0 @@
-print("Starting training process...")
-from datasets import load_dataset
-from transformers import (
-    AutoModelForSeq2SeqLM,
-    AutoTokenizer,
-    Trainer,
-    DataCollatorForSeq2Seq
-)
-from training_config import training_args
-
-# Load dataset
-dataset = load_dataset("health360/Healix-Shot", split=f"train[:100000]")
-
-# Initialize model and tokenizer
-model_name = "google/flan-t5-large"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-def tokenize_function(examples):
-    return tokenizer(
-        examples['text'],
-        padding="max_length",
-        truncation=True,
-        max_length=512,
-        return_attention_mask=True
-    )
-
-# Process dataset
-train_test_split = dataset.train_test_split(test_size=0.1)
-tokenized_train = train_test_split['train'].map(
-    tokenize_function, 
-    batched=True, 
-    remove_columns=dataset.column_names
-)
-tokenized_eval = train_test_split['test'].map(
-    tokenize_function, 
-    batched=True, 
-    remove_columns=dataset.column_names
-)
-
-# Initialize trainer
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=tokenized_train,
-    eval_dataset=tokenized_eval,
-    data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)
-)
-
-# Train and save
-print("Starting the training...")
-trainer.train()
-print("Training complete, saving model...")
-model.push_to_hub("MjolnirThor/flan-t5-custom-handler")
-tokenizer.push_to_hub("MjolnirThor/flan-t5-custom-handler")
-print("Model saved successfully!")