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CHECKPOINTS_DIR=../llmcal2/outputs/checkpoints
HF_TOKEN=$(cat hf_token.txt)
model="llama3.2-1b-instruct"
# model="qwen2.5-7b-instruct"
# Reproducibility
base_seed=2834
declare -A dataset2nseeds=(
["sst2"]=9
["agnews"]=9
["dbpedia"]=5
["20newsgroups"]=5
["banking77"]=5
)
# num_seeds=9
# Supported models
declare -A model2checkpoint=(
["llama3.2-1b"]="meta-llama/Llama-3.2-1B"
["llama3.2-1b-instruct"]="meta-llama/Llama-3.2-1B-Instruct"
["qwen2.5-7b"]="Qwen/Qwen2.5-7B"
["qwen2.5-7b-instruct"]="Qwen/Qwen2.5-7B-Instruct"
)
mkdir -p $CHECKPOINTS_DIR
if [ ! -d $CHECKPOINTS_DIR/${model2checkpoint[$model]} ]; then
litgpt download ${model2checkpoint[$model]} --checkpoint_dir $CHECKPOINTS_DIR --access_token $HF_TOKEN
rm -rf $CHECKPOINTS_DIR/${model2checkpoint[$model]}/*.bin
fi
if [ ! -z ${model2checkpoint[${model}-instruct]} ]; then
if [ ! -d $CHECKPOINTS_DIR/${model2checkpoint[${model}-instruct]} ]; then
litgpt download ${model2checkpoint[${model}-instruct]} --checkpoint_dir $CHECKPOINTS_DIR --access_token $HF_TOKEN
rm -rf $CHECKPOINTS_DIR/${model2checkpoint[${model}-instruct]}/*.bin
fi
fi
# Datasets
declare -a DATASETS=(20newsgroups dbpedia sst2 agnews banking77)
# declare -a DATASETS=(sst2 agnews)
# Train sizes
# declare -a FACTORS=(8 16 32 64 128 256)
declare -a FACTORS=(16 32 64 128 256)
# Test sizes
declare -A dataset2testsize=(
["sst2"]=400
["agnews"]=400
["dbpedia"]=700
["20newsgroups"]=800
["banking77"]=1000
)
max_seq_length=2048
inference_max_seq_len=20000
export CUDA_VISIBLE_DEVICES=1 |