#!/bin/bash
# Mini pilot: shake out the full eval->predict->stats chain on real data
# (binary sst2 + multiclass ag_news) at small N, across 2 models. Cheap + fast.
cd /root/rivermind-data/probe_stability
export HF_ENDPOINT=https://hf-mirror.com HF_HOME=/root/rivermind-data/hf_cache HF_HUB_DISABLE_XET=1
export PROBE_N_TRAIN=600 PROBE_N_EVAL=400 PROBE_N_AUG=2 PROBE_BATCH=64
MODELS="pythia-70m pythia-410m"
DSETS="sst2 ag_news"
for m in $MODELS; do
  for d in $DSETS; do
    echo "=== EXTRACT $m $d ==="; python3 -u run_pipeline.py extract  --model "$m" --dataset "$d" 2>&1 | grep -vE "Loading weights|LOAD REPORT|UNEXPECTED|Notes:|can be ignored|embed_out|^Key|^---|xethub|Trying to resume|Warning: You are|Generating|Fetching"
    echo "=== EVAL    $m $d ==="; python3 -u run_pipeline.py eval     --model "$m" --dataset "$d" 2>&1 | tail -8
    echo "=== PREDICT $m $d ==="; python3 -u run_pipeline.py predict  --model "$m" --dataset "$d" 2>&1 | tail -5
  done
done
echo "=== STATS ==="; python3 -u run_pipeline.py stats 2>&1
echo "=== PILOT_DONE ==="