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model_dir=${1:-"MODELS/pllava-7b"} | |
weight_dir=${2:-"${model_dir}"} | |
num_frames=16 | |
lora_alpha=4 | |
echo Running DEMO from model_dir: ${model_dir} | |
echo Running DEMO from weights_dir: ${weight_dir} | |
echo Running DEMO On Devices: ${CUDA_VISIBLE_DEVICES} | |
# # 34B Need to Use dispatch for this large. | |
# CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES} python -m tasks.eval.demo.pllava_demo \ | |
# --pretrained_model_name_or_path ${model_dir} \ | |
# --num_frames ${num_frames} \ | |
# --use_lora \ | |
# --weight_dir ${weight_dir} \ | |
# --lora_alpha ${lora_alpha} \ | |
# --conv_mode eval_vcg_llava_next \ | |
# --use_multi_gpus \ | |
# 7B and 13B, There are problem if Model was split around A100 40G... Probably because some unkown bug in accelerate dispatch | |
CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-"0,1"} python -m tasks.eval.demo.pllava_demo \ | |
--pretrained_model_name_or_path ${model_dir} \ | |
--num_frames ${num_frames} \ | |
--use_lora \ | |
--weight_dir ${weight_dir} \ | |
--lora_alpha ${lora_alpha} \ | |
--conv_mode plain \ | |
--use_multi_gpus | |