Open pradeepdev-1995 opened 7 months ago
I am finetuning the mistral model using the following configurations
training_arguments = TrainingArguments( output_dir=output_dir, per_device_train_batch_size=per_device_train_batch_size, gradient_accumulation_steps=gradient_accumulation_steps, optim=optim, save_steps=save_steps, logging_strategy="steps", logging_steps=10, learning_rate=learning_rate, weight_decay=weight_decay, fp16=fp16, bf16=bf16, max_grad_norm=max_grad_norm, max_steps=13000, warmup_ratio=warmup_ratio, group_by_length=group_by_length, lr_scheduler_type=lr_scheduler_type ) trainer = SFTTrainer( model=peft_model, train_dataset=data, peft_config=peft_config, dataset_text_field=" column name", max_seq_length=3000, tokenizer=tokenizer, args=training_arguments, packing=packing, ) trainer.train()
during this training I am getting the multiple checkpoints in the specified output directory output_dir.
output_dir
Once the model training is over I can save the model using
trainer.save_model()
Not only that i can save the final model using
trainer.model.save_pretrained("path")
So I bit confused. Which is the actual way to store the adapter after PEFT based lora fine-tuning
whether it is 1 - Take the least loss checkpoint folder from the output_dir or 2 - save the adapter using
or 3 - this method
I am finetuning the mistral model using the following configurations
during this training I am getting the multiple checkpoints in the specified output directory
output_dir
.Once the model training is over I can save the model using
Not only that i can save the final model using
So I bit confused. Which is the actual way to store the adapter after PEFT based lora fine-tuning
whether it is 1 - Take the least loss checkpoint folder from the
output_dir
or 2 - save the adapter usingor 3 - this method