“While GPT-4o is still treating heartbeats as pixel art, Stanford has taught a 1-billion-parameter Llama to read 12-lead ECGs—cutting VRAM by 70 % and quadrupling F1, while printing a discharge summary with human-like reasoning.”

TL;DR

1. Reproduce in minutes: one Docker command turns a 1 B Llama into a “time-series specialist” that ingests ECG, EEG or accelerometer data of any length.
2. Deploy today: Gradio demo + CUDA/Mac MPS image included; offline hospital-ready pipeline in < 30 min.
3. Hack freely: open-source CoT datasets + training scripts; swap two lines to stream glucose, BP or industrial sensors.

Introduction | Why Your LLM Still Can’t Read an ECG

2 a.m. in the ICU. A monitor spits out 1 000 Hz of voltage. The resident glances at the trace; the AI assistant converts it to a PNG, feeds GPT-4o and gets:
“Sharp spikes visible—consult physician.”
The bottleneck isn’t model size—it’s modality. Continuous, high-resolution signals are not sentences or photos; they are symphonies. Forcing them through text or image tokenizers is like translating Beethoven to emojis and asking for the chorus.

OpenTSLM closes the gap by making time series a native language inside pretrained LLMs. Result: a 1 B Llama reaches 69.9 % F1 on sleep staging, leaving 200 B GPT-4o (15.5 %) in the dust while using 40 GB VRAM—versus >110 GB for the next best baseline.

1. Intuition | How a Mini-Model Crushes a Giant

1.1 Soft-prompt vs Cross-attention—The Modality War

SoftPrompt concatenates learnable patch tokens to the text context—cheap but quadratic. Flamingo squeezes any length into 64 latent vectors and lets the LLM query them via gated cross-attention. Signal length becomes irrelevant; VRAM stays flat.

1.2 Explainability—No Physician Signature, No Deploy

Five Stanford cardiologists graded 84 ECG rationales using ACC/AHA criteria. 92.9 % were judged correct or partially correct; 85.1 % appropriately incorporated patient context (medications, artifacts, age).
The model doesn’t spit labels—it writes reports:

“ST elevation ≥0.2 mV in V1-V3 with reciprocal ST depression, coupled with acute chest pain for 3 h, indicates acute anterior MI.”

2. Hands-On | Zero-to-Demo in 30 Minutes

2.1 One-liner Docker (GPU)

docker run --gpus all -p 7860:7860 \
  ghcr.io/stanfordbdhg/opentslm:1.0-cuda \
  python -m app.demo --model OpenTSLMFlamingo \
                     --checkpoint stanford-opentslm-1b-ecg

Drag a 12-lead CSV onto the Gradio UI → get a structured report <10 s.

2.2 Native Install (for hackers)

git clone https://github.com/StanfordBDHG/OpenTSLM.git
cd OpenTSLM && pip install -r requirements.txt
huggingface-cli login   # request Llama-3.2-1B access
python curriculum_learning.py --model OpenTSLMFlamingo \
                              --stages stage5_ecg_cot \
                              --device cuda --eval_only

2.3 Python API Snippet (Minimal Runnable Example)

from opentslm import FlamingoPipeline
pipe = FlamingoPipeline("stanford-opentslm-1b-ecg")
ecg = load_csv("12lead_10s_1000hz.csv")      # shape (12, 10 000)
out = pipe(ecg, prompt="What is the rhythm?")
print(out["rationale"], out["answer"])
# → Sinus rhythm with occasional PACs   Normal

Input: raw 12-lead voltage, 10 s, 1 kHz.
Output: JSON with rationale + answer, ready for EMR insertion.

3. Advanced | Plugging Your Own Sensor

3.1 The Triplet OpenTSLM Consumes

1. signal: ndarray (channels, length)
2. meta: sampling rate, unit, mean, std
3. prompt: natural-language question

{
  "signal": [[0.12, -0.05, ...], [0.11, -0.03, ...]],
  "meta": {"fs": 1000, "unit": "mV", "mean": 0.02, "std": 0.08},
  "prompt": "Does this patient have atrial fibrillation?"
}

3.2 Three Steps to Add a New Modality

1. Tweak PatchEncoder.patch_len to match sensor density (e.g., 64 for 100 Hz).
2. Inherit BaseDataset and return (signal, prompt, answer) tuples.
3. Write a prompt template—be verbose; the model loves adjectives.

4. Deep Dive | Why VRAM Drops 70 %

SoftPrompt turns 10 000 × 12 leads into 3 750 patch tokens—context explodes.
Flamingo compresses patches into 64 latent vectors; text attends via gated cross-attention. Memory complexity falls from O(L·N) to O(64·N); sequence length no longer hurts.

Figure: Flamingo flatlines; SoftPrompt goes exponential and OOM at 10k.

5. Clinical Deployment | From Weights to FDA Desk

Stanford’s clinician rubric (simplified):

1. Recognition: Did the model cite key waves (ST ↑, δ wave)?
2. Reasoning: Linked findings to the question?
3. Context: Considered age, pacemaker, lead misplacement?

Scores: 65.5 % / 62.5 % / 85.1 %.
Takeaway: good enough for first-pass screening, yet complex cases still demand human sign-off—exactly the human-in-the-loop pathway FDA likes to see.

6. FAQ

Q1: I only have 24 GB VRAM—can I play?
A: Yes, official 8-bit script ships; training fits in 20 GB, inference in 6 GB.

Q2: Chinese prompts possible?
A: Llama-3.2 tokenizer includes Chinese; just prompt in Mandarin, get Mandarin rationale.

Q3: Continuous BP forecast?
A: Verified—set patch_len = 32 for 64 Hz, swap dataset; MAE 4.2 mmHg on MIMIC-IV.

7. Next Frontiers

• ☾ On-device 1 B model detecting AFib offline from a smartwatch.
• ☾ Industrial bearings that narrate their remaining useful life.
• ☾ A clinician asking, “Tell me what happened to this patient last night—ECG, SpO₂, respiration—in one story.”

8. Take-Home Challenges

1. Ablate patch_len from 32 → 8; measure latency vs F1 and publish a 300-word report.
2. Compress Flamingo-1B to ≤6 GB while keeping F1 drop <3 % using quantization + pruning + shared cross-attention. Post your recipe as a GitHub issue.

References & Links

[1] Langer P. et al., “OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data,” arXiv:2510.02410, 2025.
[2] Official repo & Docker: https://github.com/StanfordBDHG/OpenTSLM