| # 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. |
|
|
