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zyan1deOG/nika5
nika5 is a machine learning model from zyan1deOG. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
<p align="center" <img src="assets/architecture.png" width="100%" </p
Downloads · 30 days
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Updated Aug 21, 2026
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.pt4.7 MB · 38%
From the Hugging Face model README
A world model of the Sun. 1.16M parameters of MLPs, pooling, and gates.
The task: hide one of the Sun's 9 wavelength images. Reconstruct it from the other 8 plus the magnetic field. It trains in 2,000 steps, about five minutes on one GPU.
model.py the network: encoders, latent graph, dynamics, readout
train.py masked channel training, one flag per lever
lib.py constants and shared utilities
run_eval.py the scorecard: per channel accuracy vs baselines (immutable)
evals/ metrics, baselines, physics probes (immutable)
data/ SDOML v2 fetching, alignment, caching (immutable)
tools/ rendering, cache warming, Ensue publishing
pip install torch s3fs zarr
python -m tools.warm_august
python train.py --iters 2000 --all-cached --lr 3e-3
python run_eval.py runs/nika5_v0/ckpt.pt
python -m tools.show_output runs/nika5_v0/ckpt.pt
SDOML v2 (Galvez et al.), NASA's machine learning dataset for the Solar Dynamics Observatory, read from the public S3 bucket gov-nasa-hdrl-data1. August 2010: 9 AIA wavelength channels and the HMI vector magnetogram, aligned to a shared cadence and pooled to 256 x 256. Train and validation days never mix, and the validation days are never touched by training.
Score is a harmonic mean over the 9 channels of accuracy relative to the strongest baseline, on held out days.
<video controls src="https://huggingface.co/zyan1deOG/nika5/resolve/main/assets/timelapse_v2.mp4" width="100%"></video>
Ten checkpoints, one day: the score climbing from 0.292 to 0.456 as the dream sharpens. ckpt.pt is the trained champion.