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| # stage5: slate models from the train-only base m_lr8 | |
| # c28: 28 layers, 60% of val/test -> hotkey 1/1 (uid 169) | |
| # b16: 16 layers, 35% of val/test -> hotkey hata/4 (uid 52) | |
| # d8 : 8 layers, 70% of val/test -> hotkey hata/5 (uid 53) | |
| set -e | |
| cd $HOME/mt92 | |
| export TOKENIZERS_PARALLELISM=false | |
| set -a; . ./.env; set +a | |
| curl -s -L https://huggingface.co/dasLOL/mt-extract-3g/resolve/main/manifest.json -o manifest_template.json | |
| [ -d p16m ] || ./venv/bin/python prune.py m_lr8 p16m 16 2>&1 | grep -E 'dropping|saved' | |
| [ -d p8m ] || ./venv/bin/python prune.py m_lr8 p8m 8 2>&1 | grep -E 'dropping|saved' | |
| train() { # name base epochs set | |
| echo "##### $1: base $2, $3 epochs, $4" | |
| [ -d $1 ] || OUT=$1 BASE=$2 EPOCHS=$3 LR=8e-5 TRAIN=$4 ./venv/bin/python sft.py 2>&1 | tr '\r' '\n' | grep -E "train_loss|saved|Error|error" | |
| for s in val_all test_all gen_holdout; do ./venv/bin/python evalset.py $1 ex/$s.jsonl; done | |
| ./venv/bin/python backup_now.py $1 > backup_$1.log 2>&1 && echo "$1 backed up" | |
| bash ./build_final.sh $1 32 > build_$1.log 2>&1 && grep -E "MICRO|artifact|TTFT" build_$1.log | |
| cp manifest_template.json onnx_$1/final/manifest.json | |
| ./venv/bin/python calib.py onnx_$1/final 60 > calib_$1.log 2>&1; grep -v "^probe" calib_$1.log | tr -d "\n"; echo | |
| ./venv/bin/python up_art.py onnx_$1/final onnx_$1 > up_$1.log 2>&1 && echo "staged onnx_$1" | |
| } | |
| train c28 m_lr8 4 ex/full60.jsonl | |
| train b16 p16m 4 ex/full35.jsonl | |
| train d8 p8m 8 ex/full70.jsonl | |
| echo STAGE5_DONE | |