README.md

---
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}, 
}
```