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

Libraries

How to use inclusionAI/Ring-lite-linear-preview with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/Ring-lite-linear-preview", 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-lite-linear-preview", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inclusionAI/Ring-lite-linear-preview with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/inclusionAI/Ring-lite-linear-preview

SGLang

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

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

m1ngcheng

ariG23498 HF Staff commited on Aug 18, 2025

Commit

a1f2ea6

verified ·

1 Parent(s): 807b5a6

Update README.md (#2)

Browse files

- Update README.md (2fd59d49f8284230f77050b2bb24b1c2d53bf929)

Co-authored-by: Aritra Roy Gosthipaty <ariG23498@users.noreply.huggingface.co>

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README.md +2 -1

README.md CHANGED Viewed

@@ -6,6 +6,7 @@ language:
 base_model:
 - inclusionAI/Ling-lite
 pipeline_tag: text-generation
 ---
@@ -125,4 +126,4 @@ The long reasoning sft data: [Ring-lite-distill-preview-sft-data](https://huggin
 This code repository is licensed under [the MIT License](https://huggingface.co/inclusionAI/Ring-lite-linear-preview/blob/main/LICENSE).
 ## Citation
-[TBD]

 base_model:
 - inclusionAI/Ling-lite
 pipeline_tag: text-generation
+library_name: transformers
 ---
 This code repository is licensed under [the MIT License](https://huggingface.co/inclusionAI/Ring-lite-linear-preview/blob/main/LICENSE).
 ## Citation
+[TBD]