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license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
---
# DECO-0.2B
This is the 0.2B DECO checkpoint introduced by the paper *DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices*. DECO is an improved version of our previous [BlockFFN](https://arxiv.org/pdf/2507.08771) architecture, with dense-comparable performance given the same budget of total parameters.
Links: [[Paper](https://arxiv.org/pdf/2605.10933)] [[Code](https://github.com/thunlp/DECO)]
### Quick start
You can load and use this model with `AutoTokenizer` and `AutoModelForCausalLM` from `transformers`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "SparseLLM/DECO-0.2B"
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:
```
@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},
}
```
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