Instructions to use inclusionAI/Ring-mini-linear-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/Ring-mini-linear-2.0 with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/Ring-mini-linear-2.0", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("inclusionAI/Ring-mini-linear-2.0", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/Ring-mini-linear-2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/Ring-mini-linear-2.0"
# 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/Ring-mini-linear-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/Ring-mini-linear-2.0

SGLang

How to use inclusionAI/Ring-mini-linear-2.0 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/Ring-mini-linear-2.0" \
    --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/Ring-mini-linear-2.0",
		"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/Ring-mini-linear-2.0" \
        --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/Ring-mini-linear-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/Ring-mini-linear-2.0 with Docker Model Runner:
```
docker model run hf.co/inclusionAI/Ring-mini-linear-2.0
```

Ya-Lin-Zhang commited on Oct 23, 2025

Commit

b910b96

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1 Parent(s): 6091d0f

Update README.md

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@@ -221,4 +221,15 @@ vllm serve inclusionAI/Ring-mini-linear-2.0 \
               --api-key your-api-key
 ```

               --api-key your-api-key
 ```
+#### Citation
+```shell
+@misc{lingteam2025attentionmattersefficienthybrid,
+      title={Every Attention Matters: An Efficient Hybrid Architecture for Long-Context Reasoning},
+      author={Ling Team and Bin Han and Caizhi Tang and Chen Liang and Donghao Zhang and Fan Yuan and Feng Zhu and Jie Gao and Jingyu Hu and Longfei Li and Meng Li and Mingyang Zhang and Peijie Jiang and Peng Jiao and Qian Zhao and Qingyuan Yang and Wenbo Shen and Xinxing Yang and Yalin Zhang and Yankun Ren and Yao Zhao and Yibo Cao and Yixuan Sun and Yue Zhang and Yuchen Fang and Zibin Lin and Zixuan Cheng and Jun Zhou},
+      year={2025},
+      eprint={2510.19338},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2510.19338},
+}
+```