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language:
- en
- zh
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
---
# DECO-1.2B
This is the 1.2B DECO checkpoint introduced by the paper [DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices](https://huggingface.co/papers/2605.10933).
DECO (Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices) is a sparse MoE architecture designed to match the performance of dense Transformers under identical total parameter budgets and training tokens. It is an improved version of the [BlockFFN](https://arxiv.org/pdf/2507.08771) architecture.
- **Authors:** Chenyang Song, Weilin Zhao, Xu Han, Chaojun Xiao, Yingfa Chen, Zhiyuan Liu
- **Paper:** [arXiv:2605.10933](https://huggingface.co/papers/2605.10933)
- **Code:** [https://github.com/thunlp/DECO](https://github.com/thunlp/DECO)
### Quick start
You can load and use this model with `AutoTokenizer` and `AutoModelForCausalLM` from `transformers`. Since the model uses a custom architecture, `trust_remote_code=True` is required.
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "SparseLLM/DECO-1.2B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
).to("cuda").eval()
prompt = "Mixture-of-Experts models are useful because"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
with torch.no_grad():
output = model.generate(**inputs, max_new_tokens=64, do_sample=False)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
### Citation
If you find our work useful for your research, please kindly cite our paper as follows:
```bibtex
@article{song2026deco,
title={{DECO}: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices},
author={Chenyang Song, Weilin Zhao, Xu Han, Chaojun Xiao, Yingfa Chen, Zhiyuan Liu},
journal={arXiv preprint arXiv:2605.10933},
year={2026},
url={https://arxiv.org/pdf/2605.10933},
}
``` |