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Finetuning Details

5000 training samples ([0:5000]) 100 test samples ([0:100])

training_args = SFTConfig( overwrite_output_dir=True, # Overwrite the content of the output directory per_device_train_batch_size=2, # Batch size for training per_device_eval_batch_size=2, # Batch size for evaluation gradient_accumulation_steps=5, # number of steps before optimizing gradient_checkpointing=True, # Enable gradient checkpointing #gradient_checkpointing_kwargs={"use_reentrant": False}, hat nicht funktioniert bei mir warmup_steps=10, # Number of warmup steps #max_steps=1000, # Total number of training steps num_train_epochs=1, # Number of training epochs learning_rate=5e-5, # Learning rate weight_decay=0.01, # Weight decay #optim="paged_adamw_8bit", #Keep the optimizer state and quantize it fp16=True, #Use mixed precision training #For logging and saving logging_dir='./logs', logging_strategy="steps", logging_steps=1, save_strategy="steps", save_steps=10, save_total_limit=2, # Limit the total number of checkpoints eval_strategy="steps", eval_steps=10, load_best_model_at_end=True, # Load the best model at the end of training output_dir='./results', # Output directory for checkpoints and predictions #output_dir='./results', # Output directory for checkpoints and predictions #optim="paged_adamw_8bit", # Keep the optimizer state and quantize it packing=True, dataset_text_field='text', #max_seq_length=2048, #model_max_length=2048, max_seq_length=2048, )

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