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# 定义基础变量
export VLLM_ENABLE_V1_MULTIPROCESSING=0
config="vllm"
num_limit=100
max_token=3072
ntrain=0
split="test"
log_path="log/test"
# 创建日志目录
mkdir -p ${log_path}
# Declare an associative array to store model mappings
# TODO:Replace with actual model name and path
declare -A model_dict=(
["model_name_1"]="/path/to/model1"
["model_name_2"]="/path/to/model2"
)
# Outer Loop：遍历model_dict
for exp_name in "${!model_dict[@]}"; do
model="${model_dict[$exp_name]}"
# Inner Loop：遍历不同的 eval_nppl 值
for eval_nppl in 2 3 4 5 6 7 8; do
log_file="${log_path}/${exp_name}_nppl${eval_nppl}.log" # 日志文件名包含模型名称和 eval_nppl
echo "Starting job for model: $model, eval_nppl: $eval_nppl, logging to $log_file"
# 启动评估任务
CUDA_VISIBLE_DEVICES=1 PYTHONUNBUFFERED=1 python main_eval_instruct.py \
--batch_size 100 \
--model ${model} \
--max_token ${max_token} \
--ntrain ${ntrain} \
--config "${config}_${exp_name}_nppl${eval_nppl}" \
--limit ${num_limit} \
--split ${split} \
--temperature 0.0 \
--top_p 1.0 \
--seed 0 \
--problem_type "clean" \
--output_file "${log_path}/${exp_name}_nppl${eval_nppl}.json" \
--eval_nppl ${eval_nppl} > "$log_file" 2>&1
done
done
# 等待所有后台任务完成
wait
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