Instructions to use SparseLLM/DECO-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SparseLLM/DECO-0.5B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SparseLLM/DECO-0.5B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SparseLLM/DECO-0.5B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SparseLLM/DECO-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SparseLLM/DECO-0.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SparseLLM/DECO-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SparseLLM/DECO-0.5B

SGLang

How to use SparseLLM/DECO-0.5B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SparseLLM/DECO-0.5B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SparseLLM/DECO-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SparseLLM/DECO-0.5B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SparseLLM/DECO-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SparseLLM/DECO-0.5B with Docker Model Runner:
```
docker model run hf.co/SparseLLM/DECO-0.5B
```

nielsr HF Staff commited on 3 days ago

Commit

c94564c

verified ·

1 Parent(s): e18c16c

Add library_name metadata

Browse files

This PR adds `library_name: transformers` to the YAML metadata. This ensures that the model is correctly categorized on the Hub and enables the "Use in Transformers" button. I've also verified that the existing metadata and code snippets are correct and aligned with the provided library compatibility.

Files changed (1) hide show

README.md +4 -3

README.md CHANGED Viewed

@@ -1,13 +1,14 @@
 ---
-license: apache-2.0
 language:
 - en
 - zh
 pipeline_tag: text-generation
 ---
 # DECO-0.5B
-This is the 0.5B 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)]
@@ -34,7 +35,7 @@ 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},

 ---
 language:
 - en
 - zh
+license: apache-2.0
 pipeline_tag: text-generation
+library_name: transformers
 ---
 # DECO-0.5B
+This is the 0.5B 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 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)]
 ### 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},