Downloads · 30 days
0
trm-mnist-baseline
ludwigw/trm-mnist-baseline
trm-mnist-baseline is a machine learning model from ludwigw. 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.
Tiny Recursive Model (105.8M parameters) trained on MNIST without robustness methods.
Access
Public
Updated Jun 18, 2026
Repo size
770 KB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.pt770 KB · 100%
At a glance
- License
- mit
- Access
- Public
- Created
- May 16, 2026
- Updated
- Jun 18, 2026
- SHA
- cbb6f670
Datasets
Model card
From the Hugging Face model README
TRM MNIST Baseline
Tiny Recursive Model (105.8M parameters) trained on MNIST without robustness methods.
Model Details
- Architecture: TRM-MLP (x_dim=784, y_dim=512, z_dim=512, hidden=1024, H=2, L=2)
- Parameters: 105.8M
- Training: Standard cross-entropy, 20 epochs, lr=1e-3
- Dataset: MNIST (60k train, 10k test)
Verification Results (β-CROWN, 512 samples)
| ε (L∞) | Verified | Time/sample |
|---|---|---|
| 0.01 | 12% | 0.15s |
| 0.03 | 3% | 0.18s |
| 0.06 | 1% | 0.21s |
Key Finding: Baseline fails beyond ε=0.01.
Usage
import torch
from veriphi.models import TinyRecursiveMLP
model = TinyRecursiveMLP(x_dim=784, y_dim=512, z_dim=512, hidden=1024,
num_classes=10, H_cycles=2, L_cycles=2)
model.load_state_dict(torch.load("trm-mnist-baseline.pt"))
model.eval()
x = torch.randn(1, 784)
logits = model(x)
Citation
@article{deshmukh2026veriphi,
title={Veriphi: Attack-Guided Neural Network Verification with Dataset-Dependent Training Methods},
author={Deshmukh, Pratik and Savin, Vasili and Arya, Kartik},
journal={arXiv preprint arXiv:2606.18454},
year={2026}
}
Paper: arXiv:2606.18454 | Code: GitHub
- License
- mit
- Created
- May 16, 2026
- Updated
- Jun 18, 2026