Correct the model card (pooling is at the LABELS suffix's last token); add code/classifier from project commit 4ced7554f
3d7c238 verified | # Training stages on the pod (spec section 6). Run each inside tmux; every stage resumes from last/. | |
| # The local ledger (classifier.ledger check) must say "go" before each stage starts. | |
| # run.sh smoke stage 1: 100 steps on 1k posts, stop at 50 and resume, forced step on the 8 longest | |
| # KERNELS=torch run.sh ... runs a stage on the PyTorch path (the spec's pre-declared fallback) | |
| # run.sh curve stage 2: seeded 8k train posts, up to 3 epochs, dev every 50 steps | |
| # run.sh full stage 3: all train posts, 2 epochs, dev every 200 steps | |
| # run.sh evaluate RUN stage 4: dev, benchmark (repeated) and robustness predictions; dev temperatures | |
| # run.sh pool RUN stage 5: forward-only scores for the local-context pool | |
| # run.sh e1 | e2 optional extras: post-trained checkpoint on 8k | 2k curve point | |
| set -euo pipefail | |
| W=/workspace | |
| cd "$W/code" | |
| . "${VENV:-/root/venv}/bin/activate" | |
| export HF_HOME=/root/hf | |
| DATA=$W/data/classifier-data-20261017 | |
| M=${MODELS:-/root/models} # container disk (bootstrap.sh) | |
| BASE=$M/Qwen3.5-9B-Base | |
| train() { local out=$1 hours=$2; shift 2 | |
| timeout --signal=INT -k 300 "$(python3 -c "print(int(($hours + 0.25) * 3600))")" \ | |
| python -m classifier.train --data "$DATA" --model "${MODEL:-$BASE}" --out "$out" --max-hours "$hours" \ | |
| --kernels "${KERNELS:-fast}" "$@"; } | |
| case "${1:-}" in | |
| smoke) | |
| out=$W/runs/smoke | |
| rc=0; train "$out" 0.6 --subset 1000 --epochs 4 --max-steps 100 --eval-every 50 --dev-limit 300 --log-every 5 \ | |
| --force-longest 8 --stop-after 50 || rc=$? | |
| [ $rc = 3 ] || { echo "smoke: expected exit 3 at the planned stop, got $rc"; exit 1; } | |
| train "$out" 0.6 --subset 1000 --epochs 4 --max-steps 100 --eval-every 50 --dev-limit 300 --log-every 5 --resume | |
| timeout -k 60 900 python -m classifier.predict --kernels "${KERNELS:-fast}" --run "$out" --split dev --limit 200 --repeat 2 \ | |
| --out "$out/predictions/dev-repeat.jsonl" | tee "$out/predict_repeat.json" ;; | |
| curve) | |
| train "$W/runs/curve-8k" 1.0 --subset 8000 --epochs 3 --eval-every 50 --resume ;; | |
| full) | |
| train "$W/runs/full" 2.5 --epochs 2 --eval-every 200 --resume ;; | |
| evaluate) | |
| run=${2:?run directory}; p=$run/predictions | |
| # Temperatures come from the training-time predictions of the best checkpoint (predictions/dev.jsonl). | |
| # dev-reload.jsonl re-predicts dev from the saved checkpoint: classifier_eval determinism compares the two. | |
| timeout -k 60 2700 python -m classifier.predict --kernels "${KERNELS:-fast}" --run "$run" --split dev --out "$p/dev-reload.jsonl" | tee "$run/dev_predict.json" | |
| python -m classifier.calibrate fit "$p/dev.jsonl" --data "$DATA" --out "$run/temperatures.json" | |
| timeout -k 60 2700 python -m classifier.predict --kernels "${KERNELS:-fast}" --run "$run" --split benchmark --repeat 2 --out "$p/benchmark.jsonl" \ | |
| | tee "$run/benchmark_predict.json" | |
| timeout -k 60 1800 python -m classifier.predict --kernels "${KERNELS:-fast}" --run "$run" --input "$W/inputs/robustness.jsonl" --out "$p/robustness.jsonl" \ | |
| | tee "$run/robustness_predict.json" ;; | |
| pool) | |
| run=${2:?run directory} | |
| for f in "$DATA"/pool/*.jsonl; do | |
| timeout -k 60 1800 python -m classifier.predict --kernels "${KERNELS:-fast}" --run "$run" --input "$f" --out "$run/predictions/pool-$(basename "$f")" \ | |
| | tee -a "$run/pool_predict.json" | |
| done ;; | |
| e1) | |
| MODEL=$M/Qwen3.5-9B train "$W/runs/e1-instruct-8k" 1.0 --subset 8000 --epochs 3 --eval-every 50 --resume ;; | |
| e2) | |
| train "$W/runs/curve-2k" 0.3 --subset 2000 --epochs 3 --eval-every 25 --resume ;; | |
| *) sed -n 2,10p "$0"; exit 2 ;; | |
| esac | |