metadata
library_name: transformers
license: apache-2.0
datasets:
- wmt/wmt14
Quick start guide
To use this models, follow the snippet below:
from transformers import AutoModelForMaskedLM
# model_config_overrides = {} # Use this to optionally override config parameters
model = AutoModelForMaskedLM.from_pretrained(
"kuleshov-group/e2d2-wmt",
trust_remote_code=True,
# **model_config_overrides,
)
Model details
- Trained from scratch on
wmt/wmt14 - Qwen3 tokenizer:
Qwen/Qwen3-0.6B-Base - Block diffusion parameterization, with block size 4
See the project site for more details and link to the paper and code: https://m-arriola.com/e2d2/
Citation
@inproceedings{
arriola2025e2d2,
title={Encoder-Decoder Diffusion Language Models for Efficient Training and Inference},
author={Marianne Arriola and Yair Schiff and Hao Phung and Aaron Gokaslan and Volodymyr Kuleshov},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://arxiv.org/abs/2510.22852}
}