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curl -L -o eval_fg.sh https://huggingface.co/chen459664/quantization2/resolve/0dfd8d2a71a75ba1b625c8e2b4d43e6ad0e22dfd/eval_fg.sh
2.88 kB
| export HTTP_PROXY=http://sys-proxy-rd-relay.byted.org:8118 | |
| export http_proxy=http://sys-proxy-rd-relay.byted.org:8118 | |
| export https_proxy=http://sys-proxy-rd-relay.byted.org:8118 | |
| export no_proxy="$no_proxy,.byteintl.net" | |
| export HF_ENDPOINT=https://hf-mirror.com | |
| cd quantization_metric/ | |
| model_id=/mnt/bn/life-mllm/users/cxr/quantization/models/Qwen/Qwen2.5-7B | |
| model_name=$(basename "$model_id") | |
| model=/mnt/bn/life-mllm/users/cxr/quantization/models/${model_name}-quantization-fg | |
| # output_dir=Alpha_values_mlp | |
| # bit_layers_dir=/mnt/bn/life-mllm/users/cxr/quantization/quantization_metric/bit_layers | |
| # result_dir=/mnt/bn/life-mllm/users/cxr/quantization/lm-evaluation-harness/results | |
| start=$(date +%s.%N) | |
| # rm -rf $model | |
| cd ../quantization_metric | |
| # fg1 | |
| # self_attn_layer_to_quant="4 1 2 8 23" | |
| # mlp_layer_to_quant="27 16 19 17 25" | |
| # save_fg=fg2 | |
| # self_attn_layer_to_quant="23 22 25 24 26" | |
| # mlp_layer_to_quant="27 16 19 17 25" | |
| # save_fg=fg3 | |
| # self_attn_layer_to_quant="23 22 25 24 26" | |
| # mlp_layer_to_quant="27 16 19" | |
| # save_fg=fg4 | |
| # self_attn_layer_to_quant="23 22 25 24 26" | |
| # mlp_layer_to_quant="27" | |
| # save_fg=fg5 | |
| # self_attn_layer_to_quant="27 16 19 17 25" | |
| # mlp_layer_to_quant="27 16 19 17 25" | |
| # save_fg=baseline_BI | |
| # self_attn_layer_to_quant="16 17 15 14 13" | |
| # mlp_layer_to_quant="16 17 15 14 13" | |
| save_fg=f6 | |
| self_attn_layer_to_quant="4 1 2 8 23 22 25 5 24 7 26 6 20 12 19 17 21 11 10 9 18" | |
| mlp_layer_to_quant="27 16 19" | |
| python -u main_fg.py --cuda_id 6 --save_dir ${model} --model_id $model_id --self_attn_layer_to_quant "${self_attn_layer_to_quant}" --mlp_layer_to_quant "${mlp_layer_to_quant}" | |
| cd ../lm-evaluation-harness | |
| bash run_scripts/eval_base_fg.sh ${model} ${save_fg} | |
| rm -rf ${model} | |
| end=$(date +%s.%N) | |
| runtime=$(awk "BEGIN {print $end - $start}") | |
| echo "Execution time: $runtime seconds" | |
| model_id=/mnt/bn/life-mllm/users/cxr/quantization/models/meta-llama/Llama-3.1-8B | |
| model_name=$(basename "$model_id") | |
| model=/mnt/bn/life-mllm/users/cxr/quantization/models/${model_name}-quantization-fg | |
| # output_dir=Alpha_values_mlp | |
| # bit_layers_dir=/mnt/bn/life-mllm/users/cxr/quantization/quantization_metric/bit_layers | |
| # result_dir=/mnt/bn/life-mllm/users/cxr/quantization/lm-evaluation-harness/results | |
| start=$(date +%s.%N) | |
| # rm -rf $model | |
| cd ../quantization_metric | |
| # save_fg=baseline_BI | |
| # self_attn_layer_to_quant="24 25 23 26 27" | |
| # mlp_layer_to_quant="24 25 23 26 27" | |
| save_fg=fg6 | |
| self_attn_layer_to_quant="29 23 24 30 18 28 26 20 16 27 25 17 19 21" | |
| mlp_layer_to_quant="26 20 22" | |
| python -u main_fg.py --cuda_id 6 --save_dir ${model} --model_id $model_id --self_attn_layer_to_quant "${self_attn_layer_to_quant}" --mlp_layer_to_quant "${mlp_layer_to_quant}" | |
| cd ../lm-evaluation-harness | |
| bash run_scripts/eval_base_fg.sh ${model} ${save_fg} | |
| rm -rf ${model} | |
| end=$(date +%s.%N) | |
| runtime=$(awk "BEGIN {print $end - $start}") | |
| echo "Execution time: $runtime seconds" | |