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Open-weight model · Feature extraction

starwell

by Charles Norton phanerozoic/starwell

starwell is an open-weight model for feature extraction from Charles Norton, released under MIT License. It has 26M parameters. At 16-bit it needs about 0.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 118 downloads a month.

Starwell is a generative model for stellarator design. Given a number of field periods, an aspect ratio and a target magnetic-gradient scale length LgradB, it samples a plasma boundary for the mhdstable problem of the ConStellaration benchmark, predicts its…

Parameters26M
Context—
Weights107.8 MB
Licensemit
AccessOpen weights
Monthly Downloads118

Runs On

What it takes to serve starwell (26M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.1 GB 0.1 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 12, 2026.

starwell on every accelerator the SAVRN Index prices, at every precision

Model Card

By Charles Norton, published under mit, revision d4a23c4f44e7.

Starwell is a generative model for stellarator design. Given a number of field periods, an aspect ratio and a target magnetic-gradient scale length LgradB, it samples a plasma boundary for the mhdstable problem of the ConStellaration benchmark, predicts its benchmark metrics, and lays out modular coils for it. Boundaries are SurfaceRZFourier dicts (rcos, zsin) that constellaration evaluates directly; sample also takes tau, guidance, steps and seed, and model.config.sampling holds the integrator's defaults. Coils are simsopt CurveXYZFourier coefficients for one half period with their currents, and simsopt.field.coilsviasymmetries builds the full set; limits (in metres and per metre) sets the…

Read Charles Norton's full model card

Starwell is a generative model for stellarator design. Given a number of field periods, an aspect ratio and a target magnetic-gradient scale length L_grad_B, it samples a plasma boundary for the mhd_stable problem of the ConStellaration benchmark, predicts its benchmark metrics, and lays out modular coils for it.

Usage

from transformers import AutoModel

model = AutoModel.from_pretrained("phanerozoic/starwell", trust_remote_code=True)

boundaries = model.sample(nfp=[3, 4], aspect_ratio=[8.0, 16.0], l_grad_b=[6.5, 21.5])
open("submission.json", "w").write(model.to_submission(boundaries))

design = model.design(nfp=3, aspect_ratio=8.0, l_grad_b=6.5)[0]
design["boundary"], design["predicted"], design["coils"]

Boundaries are SurfaceRZFourier dicts (r_cos, z_sin) that constellaration evaluates directly; sample also takes tau, guidance, steps and seed, and model.config.sampling holds the integrator's defaults. Coils are simsopt CurveXYZFourier coefficients for one half period with their currents, and simsopt.field.coils_via_symmetries builds the full set; limits (in metres and per metre) sets the coil spacing, plasma clearance and curvature they are made for, and each coil set reports the bending and twist strain of a REBCO tape wound on it (strain_met).

Model

The generator is a conditional flow-matching transformer with one token per Fourier mode of the boundary, conditioned on nfp, log aspect ratio and log L_grad_B through adaptive layer norm, trained on boundaries that satisfy all five mhd_stable constraints. A residual MLP head predicts L_grad_B and the five constrained quantities. A second predicts the coils from the boundary and the engineering limits; it was trained on the coil sets of Proxima Fusion's CoilStellaration that meet their limits and on our own optimized sets. The model also carries a library of coil sets that meet every limit. The nearest library set is tried first and the head's coils when it misses a limit, with the currents solved on the boundary; polish=N refines the better start by up to N L-BFGS iterations of a stage-two coil objective, stopping once every limit holds, and polishes the other start only if the first fails.

Configuration

Architecture
StarwellModel
Stored precision
float32
Model type
starwell

Identity and Version

Repository
phanerozoic/starwell
Publisher
Charles Norton
Task
Feature extraction
Modality
Text
Library
transformers
Parameters
26M parameters
Languages
Not stated by the source
Revision
d4a23c4f44e7d791ea21194bff2a7a326d203cd7
First published
2026-09-22
Last updated
2026-09-25

Files and Weights

7 files, 108.1 MB in total. The weights are 1 file totalling 107.8 MB in safetensors.

Weights1 file · 107.8 MB
Configuration3 files · 52.9 KB
Documentation1 file · 2.6 KB
Other1 file · 230.9 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights107.8 MB f30a75bc1510
config.jsonConfiguration2.2 KB —
configuration_starwell.pyConfiguration1.3 KB —
modeling_starwell.pyConfiguration49.4 KB —
README.mdDocumentation2.6 KB —
starwell.jpgOther230.9 KB b059b021d8a5
.gitattributesRepository1.6 KB —

License and Download

License
mit
Access
Open weights, no gate
Download size
107.8 MB
Download from Charles Norton

Released by Charles Norton through its official repository on Hugging Face. Read the license.

Memory Requirements

PrecisionWeights in memory
As published107.8 MB
16-bit0.1 GB
8-bit0.0 GB
4-bit0.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About starwell

How much GPU memory does starwell need?

About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (26M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run starwell on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use starwell commercially?

Yes. starwell is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

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