eugenehp/BrainHarmony

Brain-Harmony Pretrained Weights (SafeTensors)

Pretrained weights for the Brain-Harmony multimodal brain foundation model, converted to SafeTensors format for use with the brainharmony-rs Rust inference crate.

Models

File	Model	Description	Params	Size
harmonizer.safetensors	OneTokRegViT	Stage 1 pretrained encoder-decoder (fMRI + T1)	90M	466 MB
harmonix-f.safetensors	FlexVisionTransformer	fMRI encoder + JEPA predictor	~150M	723 MB
harmonix-s.safetensors	OneTokRegViT	T1 structural encoder-decoder	85M	448 MB

Position Embedding Files

File	Description	Shape
gradient_mapping_400.csv	Brain gradient coordinates (30 axes)	400 ROIs x 30
schaefer400_roi_eigenmodes.csv	Geometric harmonics (Schaefer 400 parcellation)	400 ROIs x 200

Architecture

Brain-Harmony is a ViT-Base encoder (12 layers, 768-dim, 12 heads) that processes parcellated brain signals through:

• FlexiPatchEmbed: Conv2d with dynamic patch size (default 48)
• Brain gradient + geometric harmonics positional embeddings: combines spatial gradient mapping with cortical eigenmode projections
• JEPA framework: self-supervised pretraining with masked prediction

Input: [B, 1, 400, 864] (400 cortical ROIs x 18 patches x 48 timepoints) Output: [B, 7200, 768] latent embeddings

Usage with Rust (brainharmony-rs)

use brainharmony::{BrainHarmonyEncoder, ModelConfig, DataConfig};
use burn::backend::NdArray;

type B = NdArray;

let device = burn::backend::ndarray::NdArrayDevice::Cpu;
let (encoder, ms) = BrainHarmonyEncoder::<B>::from_weights(
    "harmonizer.safetensors",
    "gradient_mapping_400.csv",
    "schaefer400_roi_eigenmodes.csv",
    &ModelConfig::default(),
    &DataConfig::default(),
    &device,
)?;

let result = encoder.encode_safetensors("input_signal.safetensors")?;
result.save_safetensors("embeddings.safetensors")?;

Usage with Python (Brain-Harmony)

import torch
from safetensors.torch import load_file

weights = load_file("harmonizer.safetensors")
# Load into your Brain-Harmony model
model.load_state_dict(weights)

Conversion

These weights were converted from PyTorch .pth checkpoints using:

python scripts/convert_weights.py \
    --input checkpoints/harmonizer/model.pth \
    --output data/harmonizer.safetensors

License

MIT

Citation

@software{brainharmony_rs,
  title  = {brainharmony-rs: Brain-Harmony inference in Rust},
  author = {Eugene Hauptmann},
  url    = {https://github.com/eugenehp/brainharmony-rs},
  year   = {2025}
}