Instructions to use inclusionAI/GroveMoE-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/GroveMoE-Base with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/GroveMoE-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inclusionAI/GroveMoE-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("inclusionAI/GroveMoE-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inclusionAI/GroveMoE-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/inclusionAI/GroveMoE-Base

SGLang

How to use inclusionAI/GroveMoE-Base 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 "inclusionAI/GroveMoE-Base" \
    --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": "inclusionAI/GroveMoE-Base",
		"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 "inclusionAI/GroveMoE-Base" \
        --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": "inclusionAI/GroveMoE-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/GroveMoE-Base with Docker Model Runner:
```
docker model run hf.co/inclusionAI/GroveMoE-Base
```

GroveMoE-Base / README.md

HaoxingChen

Update README.md

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metadata

library_name: transformers
license: apache-2.0
pipeline_tag: text-generation

GroveMoE-Base

🤗 Models | 📑 Paper | 🔗 Github

Highlights

We introduce GroveMoE, a new sparse architecture using adjugate experts for dynamic computation allocation, featuring the following key highlights:

Architecture: Novel adjugate experts grouped with ordinary experts; shared computation is executed once, then reused, cutting FLOPs.
Sparse Activation: 33 B params total, only 3.14–3.28 B active per token.
Traning: Mid-training + SFT, up-cycled from Qwen3-30B-A3B-Base; preserves prior knowledge while adding new capabilities.

Model Downloads

Model	#Total Params	#Activated Params	HF Download	MS Download
GroveMoE-Base	33B	3.14~3.28B	🤗 HuggingFace	📦 ModelScope
GroveMoE-Inst	33B	3.14~3.28B	🤗 HuggingFace	📦 ModelScope

Citation

@article{GroveMoE,
title = {GroveMoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts},
author = {Wu, Haoyuan and Chen, Haoxing and Chen, Xiaodong and Zhou, Zhanchao and Chen, Tieyuan and Zhuang, Yihong and Lu, Guoshan and Zhao, Junbo and Liu, Lin and Huang, Zenan and Lan, Zhenzhong and Yu, Bei and Li, Jianguo},
journal = {arXiv preprint arXiv:2508.07785},
year = {2025}
}