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SixAILab/
nepa-large-patch14-224-sft

Name: nepa-large-patch14-224-sft
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SixAILab/nepa-large-patch14-224

finetune

apache-2.0

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NEPA: Next-Embedding Prediction Makes Strong Vision Learners

Paper

Project Page

GitHub

This is a PyTorch/GPU re-implementation of Next-Embedding Prediction Makes Strong Vision Learners.

Next-Embedding Predictive Autoregression. An image is split into patches and embedded into a sequence. An autoregressive model predicts the next embedding from previous ones.

@article{six2025nepa,
  title={Next-Embedding Prediction Makes Strong Vision Learners},
  author={Sihan Xu and Ziqiao Ma and Wenhao Chai and Xuweiyi Chen and Weiyang Jin and Joyce Chai and Saining Xie and Stella X. Yu},
  journal={arXiv preprint arXiv: 2512.16922},
  year={2025}
}

Environment

The codebase has been tested with the following environment:

Python 3.10
PyTorch 2.8.0
Transformers 4.56.2

Installation

First, clone the repository:

bash

git clone https://github.com/SihanXU/nepa
cd nepa

Then, create a conda environment and install dependencies:

bash

conda env create -f environment.yml
conda activate nepa

Alternatively, you can install the dependencies manually:

pip install -r requirements.txt

Quick Start

Here's a simple example to run inference with a pretrained NEPA model:

python

from transformers import AutoImageProcessor
from models.vit_nepa import ViTNepaForImageClassification
from PIL import Image
import requests

url = 'https://raw.githubusercontent.com/pytorch/hub/master/images/dog.jpg'
image = Image.open(requests.get(url, stream=True).raw)

processor = AutoImageProcessor.from_pretrained('SixAILab/nepa-large-patch14-224-sft')
model = ViTNepaForImageClassification.from_pretrained('SixAILab/nepa-large-patch14-224-sft')

inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

Setup Huggingface Token

To download pretrained models from Hugging Face Hub, you need to authenticate with your Hugging Face account:

hf auth login

[Optional] Setup Wandb Token

We use Wandb to track experiments. You may want to use Weights & Biases to log and track your experiments:

bash

pip install wandb
wandb login

Prepare ImageNet-1k Dataset

We use the ImageNet-1k dataset for training and evaluation. To download the dataset via Hugging Face Datasets:

python download_dataset.py

This script will download and prepare the ImageNet-1k dataset. Note that this requires approximately 150GB of disk space. You may need to accept the dataset terms on Hugging Face before downloading.

Evaluate Nepa for Image Classification

We provide pretrained checkpoints for NEPA models. The following table compares our reproduced results with the paper:

Model	SwiGLU (paper)	GeLU (reproduce)
Nepa-B	83.8	83.75
Nepa-L	85.3	85.40

To evaluate the base model on ImageNet-1k validation set:

bash scripts/eval/nepa_b_sft_eval.sh

This should give:

***** eval metrics *****
  eval_accuracy               =     0.8375
  eval_loss                   =     0.7169

To evaluate the large model:

bash scripts/eval/nepa_l_sft_eval.sh

This should give:

***** eval metrics *****
  eval_accuracy               =      0.854
  eval_loss                   =     0.6371

Fine-tune

To fine-tune a pretrained NEPA model on ImageNet-1k for image classification:

For the base model:

bash scripts/finetune/nepa_b_sft.sh

For the large model:

bash scripts/finetune/nepa_l_sft.sh

You can modify the training hyperparameters (learning rate, batch size, epochs, etc.) in the corresponding script files.

Pretrain

To pretrain NEPA from scratch on ImageNet-1k:

For the base model:

bash scripts/pretrain/nepa_b.sh

For the large model:

bash scripts/pretrain/nepa_l.sh

Pretraining typically requires multiple GPUs. We recommend using at least 8 A100 GPUs for the large model.

Convert a Pretrained Model to Classification Model

After pretraining, you can convert the pretrained model to a classification model by initializing a classification head. Use the init_nepa_cls_from_pretrain.py script:

Here is an example:

python init_nepa_cls_from_pretrain.py \
  --pretrained_model_id SixAILab/nepa-base-patch14-224 \
  --config_model_id configs/finetune/nepa-base-patch14-224-sft \
  --pretrained_revision main \
  --save_local \
  --local_dir ./nepa-base-patch14-224-sft

Acknowledgements

We gratefully acknowledge the developers of Transformers, Evaluate, and timm for their excellent open-source contributions.

Contact

Feel free to contact me through email (sihanxu@umich.edu). Enjoy!