OpenTSLM/OpenTSLM

Health v2 · maintenance only

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1211 stars · 113 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

OpenTSLM is a family of Time-Series Language Models that integrate time series as a native modality into pretrained LLMs (Llama, Gemma), enabling natural-language reasoning over multivariate medical text and time-series data. It is a Python library available on PyPI, released under the MIT license by researchers from Stanford, ETH Zurich, and collaborators.

Use cases

• reason over ECG time series with natural language questions
• sleep staging from EEG readings using an LLM
• human activity recognition from accelerometer data
• generate captions and rationales for time series
• build patient-facing digital health applications with temporal reasoning
• fine-tune a language model on multivariate time-series data

When to choose

• you need an LLM that natively understands time-series data alongside text
• you are doing research on multimodal temporal reasoning in medicine
• you want an open-source, MIT-licensed TSLM with pretrained checkpoints
• your tasks include ECG QA, sleep staging, HAR, or time-series captioning

When to avoid

• you only need classical time-series forecasting without language reasoning
• you cannot access gated Hugging Face models like Llama or Gemma
• you need a production-ready clinical system rather than a research library
• you lack GPU resources for training or inference

Facets

library · maturity active

machine-learning llm-training llm-inference nlp machine-learning large-language-models healthcare time-series artificial-intelligence python cross-platform time-series-language-model multimodal medical-ai ecg eeg huggingface research time-series-analysis gpu

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