Create README.md

README.md

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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}, 
+}
+```