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

Libraries

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

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

pipe = pipeline("text-generation", model="inclusionAI/Ring-mini-sparse-2.0-exp", 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-sparse-2.0-exp", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Failed to install SGLang

by P1atinum - opened Nov 1, 2025

Discussion

P1atinum

Nov 1, 2025

when I followed the installation guide for SGLang, I ran into the following errors.

pip install sglang==0.5.3 sgl-kernel==0.3.15 torch==2.8.0 torchvision==0.23.0 torchao
ERROR: Cannot install sgl-kernel==0.3.15 and sglang==0.5.3 because these package versions have conflicting dependencies.

The conflict is caused by:
The user requested sgl-kernel==0.3.15
sglang 0.5.3 depends on sgl-kernel==0.3.14.post1

Additionally, some packages in these conflicts have no matching distributions available for your environment:
sgl-kernel

To fix this you could try to:

loosen the range of package versions you've specified
remove package versions to allow pip to attempt to solve the dependency conflict

ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts

bestfleer

inclusionAI org Nov 4, 2025

when I followed the installation guide for SGLang, I ran into the following errors.

pip install sglang==0.5.3 sgl-kernel==0.3.15 torch==2.8.0 torchvision==0.23.0 torchao
ERROR: Cannot install sgl-kernel==0.3.15 and sglang==0.5.3 because these package versions have conflicting dependencies.

The conflict is caused by:
The user requested sgl-kernel==0.3.15
sglang 0.5.3 depends on sgl-kernel==0.3.14.post1

Additionally, some packages in these conflicts have no matching distributions available for your environment:
sgl-kernel

To fix this you could try to:

loosen the range of package versions you've specified

remove package versions to allow pip to attempt to solve the dependency conflict

ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts

In my environment, the versions are sglang==0.5.3rc0 and sgl-kernel==0.3.15.

bestfleer changed discussion status to closed Nov 17, 2025

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