# TimeAgent — Llama-3.2-1B baseline: finish the ECG eval

This bundle lets another server complete the ONLY remaining step for the
Llama-3.2-1B OpenTSLM-SoftPrompt **baseline**: the stage-5 ECG-QA-CoT evaluation.
Stages 1-4 (TSQA, M4, HAR, Sleep) are fully complete, and ECG **training** is
done (early-stopped @ epoch 14). Only the ECG generation eval remains — it kept
OOM-crashing on our shared/contended GPUs.

## Contents
- `results/Llama_3_2_1B/` — all stage checkpoints + metrics (stages 1-4 have
  metrics.json so they're skipped; stage5_ecg_cot has the trained checkpoint,
  no metrics.json → the eval will run).
- `data/ecg_qa_cot/` — ECG-QA-CoT CSV splits (train/val/test).
- `data/ptbxl.tar.gz` — PTB-XL records, tarred. On target: `cd data && tar xzf ptbxl.tar.gz` before running.
- `code/OpenTSLM/` — the code (includes our fixes: greedy eval `do_sample=False`,
  sdpa attention, left-padded batched generation, `EVAL_BATCH_SIZE` env).
- `requirements-key.txt` — key package versions we ran with.

## Setup on the target server
```bash
# 1) python env (Python 3.13; a venv or conda env)
python3 -m venv venv && source venv/bin/activate
pip install torch transformers peft wfdb datasets accelerate huggingface_hub numpy scipy pandas einops open_flamingo
#   (match requirements-key.txt if you hit version issues)

# 2) wire the checkpoints + data into the code tree (symlinks)
cd code/OpenTSLM
ln -sfn "$(realpath ../../results)"      results
ln -sfn "$(realpath ../../data)"         src/data

# 3) HF token (Llama-3.2-1B is gated; the base model auto-downloads)
export HF_TOKEN=<your_hf_token_with_llama_access>
export HF_HOME=/path/with/space/hf_cache
```

## Run the ECG eval
```bash
cd code/OpenTSLM
# On a CLEAN 95GB GPU (no other tenants) you can batch the eval for speed:
EVAL_BATCH_SIZE=4 python3 -u curriculum_learning.py --model OpenTSLMSP --llm_id meta-llama/Llama-3.2-1B
#   If it OOMs, drop to EVAL_BATCH_SIZE=2, or omit it for batch=1 (slow but always fits).
```
It will: skip stages 1-4 (metrics exist) → load the stage5 ECG checkpoint →
skip training (done) → run the ECG eval → write
`results/Llama_3_2_1B/OpenTSLMSP/stage5_ecg_cot/results/metrics.json`.
When that file appears, the 5-stage baseline is COMPLETE.

Note: eval is greedy (deterministic). ECG sequences are ~7.7k tokens, so batched
generation is memory-heavy — batch=4 needs a mostly-free 95GB GPU.