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SeismoGPT
wesmail/SeismoGPT
SeismoGPT is a other model from wesmail. Use it for the other task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
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.
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Public
Updated Jun 3, 2026
Repo size
625 MB
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Repo makeup
Click a slice to open those files.
.ckpt312 MB · 100%
At a glance
- Task
- other
- Library
- pytorch
- License
- mit
- Access
- Public
- Created
- Jun 3, 2026
- Updated
- Jun 3, 2026
- SHA
- 803243a0
Model card
From the Hugging Face model README
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 — arXiv:2606.02912v1
Code: 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
Download weights
pip install huggingface_hub
huggingface-cli download wesmail/SeismoGPT \
epoch=12-step=633750.ckpt train_phase1_logcosh.yaml \
--local-dir phase1
Or from 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.
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) |
- Task
- other
- Library
- pytorch
- License
- mit
- Created
- Jun 3, 2026
- Updated
- Jun 3, 2026