Instructions to use inclusionAI/Ring-1T-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/Ring-1T-preview with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/Ring-1T-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-1T-preview", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inclusionAI/Ring-1T-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/Ring-1T-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-1T-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/Ring-1T-preview

SGLang

How to use inclusionAI/Ring-1T-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-1T-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-1T-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-1T-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-1T-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/Ring-1T-preview with Docker Model Runner:
```
docker model run hf.co/inclusionAI/Ring-1T-preview
```

Update model card with technical report citation

#11

by RichardBian - opened Oct 22, 2025

base: refs/heads/main

←

from: refs/pr/11

Discussion Files changed

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README.md +9 -0

README.md CHANGED Viewed

@@ -112,3 +112,12 @@ This code repository is licensed under [the MIT License](https://github.com/incl
 ## Tip
 To facilitate academic research and downstream applications with customizable model naming, we did not conduct specific identity recognition training.

 ## Tip
 To facilitate academic research and downstream applications with customizable model naming, we did not conduct specific identity recognition training.
+## Reference
+```
+@article{ling2025everystep,
+  title={Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale Thinking Model},
+  author={Team, Ling and Shen, Anqi and Li, Baihui and Hu, Bin and Jing, Bin and Chen, Cai and Huang, Chao and Zhang, Chao and Yang, Chaokun and Lin, Cheng and Wen, Chengyao and Li, Congqi and Zhao, Deng and Yuan, Dingbo and You, Donghai and Mao, Fagui and Meng, Fanzhuang and Xu, Feng and Li, Guojie and Wang, Guowei and Dai, Hao and Zheng, Haonan and others},
+  journal={arXiv preprint arXiv:2510.18855},
+  year={2025}
+}
+```