#!/usr/bin/env bash
# On the pod: after the first teacher run finishes, generate more teacher samples for other prompts
# (small shards so a stop loses little; train.py scans shards directly).
while pgrep -f "rapid_moss.teachergen --model" > /dev/null; do sleep 15; done
cd /workspace/code
torchrun --nproc-per-node 8 -m rapid_moss.teachergen --model /workspace/models/MOSS-Image-SFT --cache /workspace/cache \
    --prompts /workspace/prompts/train.jsonl --out /workspace/teacher2 --count 4000 --seed 8 --shard-size 25 \
    --exclude /workspace/teacher/index.json