--- license: mit tags: - seismology - earthquake - waveform-prediction - transformer - pytorch - pytorch-lightning library_name: pytorch pipeline_tag: other --- # SeismoGPT (Phase 1) Pre-trained **Phase 1** weights for **SeismoGPT**: a causal RoPE transformer over seismic waveform tokens (Z, N, E), trained with log-cosh loss and multi-horizon auxiliary heads. This checkpoint is the deterministic model used for paper rollouts and figures in the companion code repository. **Paper:** [Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures](https://arxiv.org/abs/2606.02912v1) — [arXiv:2606.02912v1](https://arxiv.org/abs/2606.02912v1) **Code:** [github.com/wesmail/SeismoGPT](https://github.com/wesmail/SeismoGPT) ## Files | File | Description | |------|-------------| | `epoch=12-step=633750.ckpt` | PyTorch Lightning checkpoint (Phase 1, epoch 12) | | `train_phase1_logcosh.yaml` | Training configuration used for Phase 1 | ## Requirements - Python 3.10+ - `torch`, `lightning`, `seisbench`, and the [SeismoGPT code repository](https://github.com/wesmail/SeismoGPT) ## Download weights ```bash pip install huggingface_hub huggingface-cli download wesmail/SeismoGPT \ epoch=12-step=633750.ckpt train_phase1_logcosh.yaml \ --local-dir phase1 ``` Or from Python: ```python from huggingface_hub import hf_hub_download ckpt = hf_hub_download( repo_id="wesmail/SeismoGPT", filename="epoch=12-step=633750.ckpt", local_dir="phase1", ) ``` Use the same `kernel_size` (16) and `num_tokens` (320) as in training; these are stored in the checkpoint hyperparameters. For evaluation (configurations A/B/C), rollouts, and plotting, see the [code repository](https://github.com/wesmail/SeismoGPT). ## Model summary | Setting | Value | |---------|--------| | Phase | 1 (deterministic, `log_cosh`) | | Channels | Z, N, E | | Token size K | 16 samples | | Tokens per window | 320 | | `d_model` | 512 | | Encoder layers | 8 | | Prediction horizons (train) | 4 (inference uses horizon 1) |