You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Great job! After pretraining, I want to use Lora to finetune. Can I simply follow the LLava https://github.com/haotian-liu/LLaVA/tree/main, just add --lora_enable True --lora_r 128 --lora_alpha 256 --mm_projector_lr 2e-5 in the finetunig script?
I noticed that you comment out a section of the code in the train.py: # if training_args.lora_enable: # state_dict = get_peft_state_maybe_zero_3( # model.named_parameters(), training_args.lora_bias # ) # non_lora_state_dict = get_peft_state_non_lora_maybe_zero_3( # model.named_parameters() # ) # if training_args.local_rank == 0 or training_args.local_rank == -1: # model.config.save_pretrained(training_args.output_dir) # model.save_pretrained(training_args.output_dir, state_dict=state_dict) # torch.save(non_lora_state_dict, os.path.join(training_args.output_dir, 'non_lora_trainables.bin')) # else: # safe_save_model_for_hf_trainer(trainer=trainer, # output_dir=training_args.output_dir)
Will this have an effect on the trained model?
The text was updated successfully, but these errors were encountered:
Great job! After pretraining, I want to use Lora to finetune. Can I simply follow the LLava https://github.com/haotian-liu/LLaVA/tree/main, just add --lora_enable True --lora_r 128 --lora_alpha 256 --mm_projector_lr 2e-5 in the finetunig script? I noticed that you comment out a section of the code in the train.py: # if training_args.lora_enable: # state_dict = get_peft_state_maybe_zero_3( # model.named_parameters(), training_args.lora_bias # ) # non_lora_state_dict = get_peft_state_non_lora_maybe_zero_3( # model.named_parameters() # ) # if training_args.local_rank == 0 or training_args.local_rank == -1: # model.config.save_pretrained(training_args.output_dir) # model.save_pretrained(training_args.output_dir, state_dict=state_dict) # torch.save(non_lora_state_dict, os.path.join(training_args.output_dir, 'non_lora_trainables.bin')) # else: # safe_save_model_for_hf_trainer(trainer=trainer, # output_dir=training_args.output_dir)
Great job! After pretraining, I want to use Lora to finetune. Can I simply follow the LLava https://github.com/haotian-liu/LLaVA/tree/main, just add --lora_enable True --lora_r 128 --lora_alpha 256 --mm_projector_lr 2e-5 in the finetunig script?
I noticed that you comment out a section of the code in the train.py:
# if training_args.lora_enable: # state_dict = get_peft_state_maybe_zero_3( # model.named_parameters(), training_args.lora_bias # ) # non_lora_state_dict = get_peft_state_non_lora_maybe_zero_3( # model.named_parameters() # ) # if training_args.local_rank == 0 or training_args.local_rank == -1: # model.config.save_pretrained(training_args.output_dir) # model.save_pretrained(training_args.output_dir, state_dict=state_dict) # torch.save(non_lora_state_dict, os.path.join(training_args.output_dir, 'non_lora_trainables.bin')) # else: # safe_save_model_for_hf_trainer(trainer=trainer, # output_dir=training_args.output_dir)
Will this have an effect on the trained model?
The text was updated successfully, but these errors were encountered: