52 lines
1.3 KiB
Bash
52 lines
1.3 KiB
Bash
#!/bin/bash
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# We can use this script to compute baseline accuracy on GSM for vllm.
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# We use this for fp8, which HF does not support.
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#
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# Make sure you have lm-eval-harness installed:
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# pip install lm-eval==0.4.4
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usage() {
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echo``
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echo "Runs lm eval harness on GSM8k using huggingface transformers."
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echo "This pathway is intended to be used to create baselines for "
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echo "our automated nm-test-accuracy workflow"
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echo
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echo "usage: ${0} <options>"
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echo
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echo " -m - huggingface stub or local directory of the model"
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echo " -b - batch size to run the evaluation at"
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echo " -l - limit number of samples to run"
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echo " -f - number of fewshot samples to use"
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echo " -t - tensor parallel size to run at"
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echo
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}
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while getopts "m:b:l:f:t:" OPT; do
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case ${OPT} in
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m )
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MODEL="$OPTARG"
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;;
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b )
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BATCH_SIZE="$OPTARG"
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;;
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l )
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LIMIT="$OPTARG"
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;;
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f )
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FEWSHOT="$OPTARG"
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;;
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t )
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TP_SIZE="$OPTARG"
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;;
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\? )
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usage
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exit 1
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;;
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esac
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done
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lm_eval --model vllm \
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--model_args "pretrained=$MODEL,tensor_parallel_size=$TP_SIZE,distributed_executor_backend=ray,trust_remote_code=true,max_model_len=4096" \
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--tasks gsm8k --num_fewshot "$FEWSHOT" --limit "$LIMIT" \
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--batch_size "$BATCH_SIZE"
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