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# export CUDA_VISIBLE_DEVICES=0,3,4,5,6,7
export OPENAI_API_KEY=...
num_frames=16
test_ratio=200
model_dir=MODELS/pllava-34b
weight_dir=MODELS/pllava-34b
SAVE_DIR=test_results/test_pllava_34b
lora_alpha=4
conv_mode=eval_vcg_llavanext
python -m tasks.eval.vcgbench.pllava_eval_vcgbench \
--pretrained_model_name_or_path ${model_dir} \
--save_path ${SAVE_DIR}/vcgbench \
--num_frames ${num_frames} \
--use_lora \
--lora_alpha ${lora_alpha} \
--weight_dir ${weight_dir} \
--pooling_shape 16-12-12 \
--test_ratio ${test_ratio} \
--conv_mode $conv_mode
conv_mode=eval_mvbench_llavanext
python -m tasks.eval.mvbench.pllava_eval_mvbench \
--pretrained_model_name_or_path ${model_dir} \
--save_path ${SAVE_DIR}/mvbench \
--use_lora \
--lora_alpha ${lora_alpha} \
--num_frames ${num_frames} \
--weight_dir ${weight_dir} \
--pooling_shape 16-12-12 \
--conv_mode $conv_mode
conv_mode=eval_videoqa_llavanext
python -m tasks.eval.videoqabench.pllava_eval_videoqabench \
--pretrained_model_name_or_path ${model_dir} \
--save_path ${SAVE_DIR}/videoqabench \
--num_frames ${num_frames} \
--use_lora \
--lora_alpha ${lora_alpha} \
--weight_dir ${weight_dir} \
--test_ratio ${test_ratio} \
--conv_mode ${conv_mode}
conv_mode=eval_recaption_llavanext
python -m tasks.eval.recaption.pllava_recaption \
--pretrained_model_name_or_path ${model_dir} \
--save_path ${SAVE_DIR}/recaption \
--num_frames ${num_frames} \
--use_lora \
--weight_dir ${weight_dir} \
--lora_alpha ${lora_alpha} \
--test_ratio ${test_ratio} \
--conv_mode $conv_mode
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