Add library_name metadata and improve model card (#1)

- Add library_name metadata and improve model card (994942042939ed2963601b29a8ef03571f7f5bde)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,23 +1,30 @@
 ---
-license: apache-2.0
 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*. 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)]
+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`.
+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(
@@ -25,16 +32,20 @@ model = AutoModelForCausalLM.from_pretrained(
     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},
@@ -42,4 +53,4 @@ If you find our work useful for your research, please kindly cite our paper as f
       year={2026},
       url={https://arxiv.org/pdf/2605.10933}, 
 }
-```
+```