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60b21d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | #!/bin/bash
# ============================================================================
# OpenTSLM A0 BASELINE — full curriculum on THEIR original datasets
# (stage1 TSQA -> stage2 M4 -> stage3 HAR -> stage4 Sleep -> stage5 ECG)
# Usage: bash run_baseline.sh <llm_id> <gpu>
# e.g. bash run_baseline.sh google/gemma-3-270m 0
# bash run_baseline.sh meta-llama/Llama-3.2-1B 4
# ============================================================================
# ---- HF TOKEN (needs access to gated Llama/Gemma models) -------------------
export HF_TOKEN=<HF_TOKEN_REMOVED_SET_YOUR_OWN>
# --------------------------------------------------------------------------
export HF_HOME=/mnt/nvme2/adinath/timeagent/hf_cache # model + TSQA cache (nvme2, fresh disk)
export TMPDIR=/mnt/nvme2/adinath/timeagent/tmp # keep temp off the full root/nvme0
export TOKENIZERS_PARALLELISM=false
# Reclaim reserved-but-unallocated memory / reduce fragmentation on the long
# 12-lead ECG soft-prompt sequences (stage5) that otherwise OOM under GPU contention.
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
LLM_ID="${1:-google/gemma-3-270m}" # backbone (arg 1, default gemma-3-270m)
GPU="${2:-0}" # GPU (arg 2)
export CUDA_VISIBLE_DEVICES=$GPU
SAFE=$(echo "$LLM_ID" | sed 's#.*/##; s/[.-]/_/g')
LOG=/mnt/nvme2/adinath/timeagent/logs/opentslm_baseline_${SAFE}.log
# Call the venv python by absolute path: `source activate` lands in conda's
# base env here (conda auto-activation hijacks PATH). venv is on nvme2 now.
VENV_PY=/mnt/nvme2/timeagent/venv/bin/python3
export PYTHONPATH=/home/mbz-imran/Adinath/TimeAgent/OpenTSLM/src
cd /home/mbz-imran/Adinath/TimeAgent/OpenTSLM # results/ -> nvme symlink
echo "=== OpenTSLM baseline | llm=$LLM_ID | GPU=$GPU | log=$LOG ==="
"$VENV_PY" -u curriculum_learning.py \
--model OpenTSLMSP \
--llm_id "$LLM_ID" \
2>&1 | tee "$LOG"
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