llava-v1.5-7b-task-lora-20240804_042004

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echo "Starting LoRA fine-tuning..." deepspeed LLaVA/llava/train/train_mem.py
--lora_enable True --lora_r 128 --lora_alpha 256 --mm_projector_lr 2e-5
--deepspeed ./LLaVA/scripts/zero3.json
--model_name_or_path liuhaotian/llava-v1.5-7b
--version v1
--data_path "${output_dir}/processed_dataset_correct_path.json"
--image_folder "${image_dir}"
--vision_tower openai/clip-vit-large-patch14-336
--mm_projector_type mlp2x_gelu
--mm_vision_select_layer -2
--mm_use_im_start_end False
--mm_use_im_patch_token False
--image_aspect_ratio pad
--group_by_modality_length True
--bf16 True
--output_dir "${output_dir}/checkpoints/${model_name}"
--num_train_epochs 1
--per_device_train_batch_size 3
--per_device_eval_batch_size 3
--gradient_accumulation_steps 10
--evaluation_strategy "no"
--save_strategy "steps"
--save_steps 50000
--save_total_limit 1
--learning_rate 2e-4
--weight_decay 0.
--warmup_ratio 0.03
--lr_scheduler_type "cosine"
--logging_steps 1
--tf32 True
--model_max_length 2048
--gradient_checkpointing True
--dataloader_num_workers 1
--lazy_preprocess True

Model Details

Model Description

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How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

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Framework versions

• PEFT 0.12.0