ParticleViT-L

ParticleViT-L

ParticleViT is a generic transformer over the constituents of a collider jet, with no physics-specific inductive bias. Each particle is one token; a single prepended class token is read out. This is the L rung (126.1M parameters, pretraining compute 1.1e20 FLOPs) of the scaling ladder from the paper Predict before you train: scaling laws for particle physics foundation models (Uslu, Nachman, Re).

• Code: https://github.com/Jaluus/ParticleViT
• Paper corpus: OmniLearned (~1.06B jets)
• All five models: ParticleViT collection (S, M, B, L, XL)

Installation

pip install "torch>=2.4" safetensors huggingface_hub

That is all you need: the model code (modeling_particlevit.py) and the input normalization (preprocessing.py) ship inside this repository and depend only on PyTorch. To grab the files explicitly:

huggingface-cli download jaluus/ParticleViT-L --local-dir ParticleViT-L

Usage

import torch
from modeling_particlevit import ParticleViT
from preprocessing import normalize, build_attn_mask

model = ParticleViT.from_pretrained("jaluus/ParticleViT-L").eval()

# X_raw: (B, 150, 9) raw OmniLearned features per particle:
#   0:4 kinematics (delta eta, delta phi, log pT, log E), 4 PID id, 5:9 vertex.
# Padded particle slots are all-zero.
X_raw = torch.zeros(1, 150, 9)
# ... fill X_raw[0, :n_particles] with your jet ...

mask = build_attn_mask(X_raw)                                   # real-particle mask
X = normalize(X_raw, "omnilearned_parametric_normalization.json", mask)
with torch.no_grad():
    logits = model(X, attn_mask=mask)                           # (B, 210) pretraining logits

For downstream tagging, replace model.head with a fresh linear head and fine-tune the full network end to end (see the paper's fine-tuning protocol and the code repository).

The parametric input normalization is required. Feeding raw features without applying omnilearned_parametric_normalization.json produces meaningless predictions.

Attention backend

This repository ships the padded SDPA inference path (F.scaled_dot_product_attention with a key-padding mask): pure PyTorch, no flash-attn or custom kernels, running on CPU or any GPU in any precision. Training instead used a mathematically-equivalent variable-length packed attention kernel (torch.nn.attention.varlen, BF16/FP16-only) purely for throughput — it strips padding and is ~70% faster. The two paths share the exact same parameters (the weights load strict=True into either), and the class-token readout matches up to floating-point precision, so predictions are the same.

If you want the bleeding-edge packed/varlen model and the full training / fine-tuning code, use the GitHub repository: https://github.com/Jaluus/ParticleViT.

Model architecture

A generic set-transformer over particle tokens with no physics inductive bias (no Lorentz equivariance, no pairwise interaction features). Design choices follow modern open language models:

• Reordered RMSNorm kept outside the residual stream (double-norm blocks).
• Query-key normalization (QK-Norm) for attention stability.
• SwiGLU feedforward with the 8/3 width convention.
• A single prepended class token for readout; no positional encoding (a jet's constituents form a set, not a sequence).
• Truncated-normal (OLMo-style) initialization; head dimension 64.

Pretraining recipe

Objective. Softmax cross-entropy over the 210-class OmniLearned label space, read from the prepended class token, with no label smoothing. An output z-loss (weight 1e-5) keeps the logits bounded; it is excluded from the reported loss.

Optimization (shared across the ladder).

• Optimizer: AdamW (beta1 0.9, beta2 0.95), weight decay 0.1 (no decay on embeddings, norm gains, or any 1-D parameter).
• Schedule: linear warmup (2000 steps) then cosine decay to 10% of the peak LR.
• No gradient clipping. BF16 mixed precision with FP32 master weights.
• Global batch size 16384 jets.
• Variable-length attention with sequence packing (removes padding, ~70% faster).
• Frozen parametric Gaussian transform on the four kinematic input features.

This model (ParticleViT-L).

Trained on the Perlmutter supercomputer (NERSC) with PyTorch distributed data parallelism. Compute is accounted as 6 FLOPs per parameter per token at the measured mean occupancy of the 150 particle slots.

Pretraining data

ParticleViT is pretrained on the OmniLearned bundle (Bhimji, Harris, Mikuni, Nachman; arXiv:2510.24066, Phys. Rev. D 113, 032020), a union of seven simulated and real jet datasets totaling ~1.06 billion training jets (~102M validation, ~68M test). Upstream code and hosting: ViniciusMikuni/OmniLearned, served from https://portal.nersc.gov/cfs/dasrepo/omnilearned/.

Clustering radius and detector handling deliberately differ across subsets (R = 0.4 / 0.8 / 1.0; Delphes vs Geant4 vs Geant3 vs real data), so the model must generalize across regimes.

Per-particle features (9 channels). Each jet is up to 150 particles:

Features 0-3 are the minimal kinematic set; 4-8 are optional and zeroed where a subset does not provide them. The four kinematic features are mapped to an approximately standard-normal distribution by the frozen parametric transform in omnilearned_parametric_normalization.json (required at inference).

Labels. A flat 210-class label space with disjoint per-dataset ranges (e.g. jetclass 2-11, jetclass2 12-199, single buckets for aspen, cms_qcd, cms_bsm, ...). The pretraining objective is classification over these 210 classes.

Files

• model.safetensors - FP32 weights (126.1M parameters).
• config.json - architecture configuration.
• modeling_particlevit.py - self-contained model (PyTorch only).
• preprocessing.py - self-contained input normalization (PyTorch only).
• omnilearned_parametric_normalization.json - frozen normalization constants.

Downstream benchmark performance

Background rejection R50 / R30 after fine-tuning (higher is better), from the paper's Table I. R50 and R30 are 1/eps_B at 50% and 30% signal efficiency:

License

Released under the MIT license.

Citation

@article{uslu2026predict,
  title  = {Predict before you train: scaling laws for particle physics foundation models},
  author = {Uslu, Jan-Lucas and Nachman, Benjamin and R\'e, Christopher},
  year   = {2026}
}

@article{bhimji2025omnilearned,
  title   = {OmniLearned: A Foundation Model Framework for All Tasks Involving Jet Physics},
  author  = {Bhimji, Wahid and Harris, Chris and Mikuni, Vinicius and Nachman, Benjamin},
  journal = {Phys. Rev. D},
  volume  = {113}, number = {3}, pages = {032020}, year = {2026},
  eprint  = {2510.24066}, archivePrefix = {arXiv}
}