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run_all_fine_tuning.sh
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41 lines (37 loc) · 1.27 KB
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#!/bin/bash
# Declare array for LLM models
#llms=("mistralai/Mistral-7B-Instruct-v0.3" "meta-llama/Meta-Llama-3-8B-Instruct" "Qwen/Qwen2.5-7B-Instruct")
llms=("$1")
# Loop over each LLM model
for llm in "${llms[@]}"; do
# Extract the second half of the LLM model for the output directory
llm_name=$(echo "$llm" | awk -F '/' '{print $2}')
# Construct the output directory name
output_dir="all_models/${llm_name}_all"
output_dir1="all_models/${llm_name}_no_stage2_all"
# Run the Python command
python finetune_llm.py \
--eeg_dataset data/block/eeg_55_95_std.pth \
--splits_path data/block/block_splits_by_image_all.pth \
--eeg_encoder_path ./eeg_encoder_55-95_40_classes \
--image_dir data/images/ \
--output "$output_dir" \
--llm_backbone_name_or_path "$llm" \
--load_in_8bit \
--bf16
python finetune_llm.py \
--eeg_dataset data/block/eeg_55_95_std.pth \
--splits_path data/block/block_splits_by_image_all.pth \
--eeg_encoder_path ./eeg_encoder_55-95_40_classes \
--image_dir data/images/ \
--output "$output_dir1" \
--llm_backbone_name_or_path "$llm" \
--no_stage2 \
--load_in_8bit \
--bf16
# Check if the command executed successfully
if [ $? -ne 0 ]; then
echo "Error with LLM $llm"
exit 1
fi
done