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 Settings

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
```

zhangdonghao commited on Apr 24, 2025

Commit

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@@ -21,7 +21,7 @@ pipeline_tag: text-generation
 ## Introduction
-Ring-lite-linear-preview is a hybrid-linear MoE LLM provided and open-sourced by InclusionAI, which has 17.1B parameters with 3.0B activated parameters. It is a long reasoning model based on hybrid-linear attention, achieving near-linear computational complexity and near-constant space complexity during inference. This model was converted from [Ling-lite-0220](https://huggingface.co/models/inclusionAI/Ling-lite), which adopts the softmax attention-based architecture. It matches the performance of DeepSeek-R1-Distill-Qwen-7B on standardized reasoning benchmarks while substantially reducing computational overhead in both training and inference phases. In certain generation speed tests based on vLLM, we observed that the throughput was more than doubled compared to softmax attention models of the same scale (e.g., Ling-lite). To the best of our knowledge, it is the first open-source hybrid-linear reasoning language model.
 ## Model Downloads
 <div align="center">

 ## Introduction
+Ring-lite-linear-preview is a hybrid-linear MoE LLM provided and open-sourced by InclusionAI, which has 17.1B parameters with 3.0B activated parameters. It is a long reasoning model based on hybrid-linear attention, achieving near-linear computational complexity and near-constant space complexity during inference. This model was converted from [Ling-lite-0220](https://huggingface.co/inclusionAI/Ling-lite), which adopts the softmax attention-based architecture. It matches the performance of DeepSeek-R1-Distill-Qwen-7B on standardized reasoning benchmarks while substantially reducing computational overhead in both training and inference phases. In certain generation speed tests based on vLLM, we observed that the throughput was more than doubled compared to softmax attention models of the same scale (e.g., Ling-lite). To the best of our knowledge, it is the first open-source hybrid-linear reasoning language model.
 ## Model Downloads
 <div align="center">