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

Libraries

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

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

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

unfaithful IMO eval

by jordane95 - opened Oct 14, 2025

Discussion

jordane95

Oct 14, 2025

•

edited Oct 14, 2025

Hi, I noticed that your reported IMO result is purely outcome-based eval for problems requiring answers and llm-as-a-judge for proof-based ones.

In fact, I have read the provided model samples https://github.com/inclusionAI/AWorld/tree/main/examples/imo/samples/samples%20from%20Ring-1T.
All the agent traces are concluded with a final guard output that reveals some missing points in the proof, making the evaluation unfaithful.

ex. p3 guard output is

# Verification Report

## Summary

**Final Verdict:** The solution's approach is viable and arrives at the correct final answer, but contains several **Justification Gaps** that prevent it from being fully rigorous.
...

In contrast, the solutions provided by gemini deep think and oai have correct answers alongside human-verified proofs. So I think this model is far behind claimed sliver-level performance.

ziqiliu

Oct 15, 2025

Thank you for your feedback.

Please refer to: https://github.com/inclusionAI/AWorld/issues/520

ID0M

Oct 24, 2025

•

edited Oct 24, 2025

Thank you for your feedback.

Please refer to: https://github.com/inclusionAI/AWorld/issues/520

Issue author keeps moving the goalpost lol.
He stopped on "However, the solutions have not been reviewed and checked by human expert, making it still unreliable." - the thing is IMO competition has concluded and the answers are public and known. No AI lab is using independent judges for this, they either verify it themselves manually (OpenAI did this even for their own entry), or they use LLM. Nothing abnormal in this by itself unless there are solutions you can convincingly prove were rated incorrect given the expected correct answers.

jordane95

Oct 28, 2025

Thank you for your feedback.

Please refer to: https://github.com/inclusionAI/AWorld/issues/520

Issue author keeps moving the goalpost lol.
He stopped on "However, the solutions have not been reviewed and checked by human expert, making it still unreliable." - the thing is IMO competition has concluded and the answers are public and known. No AI lab is using independent judges for this, they either verify it themselves manually (OpenAI did this even for their own entry), or they use LLM. Nothing abnormal in this by itself unless there are solutions you can convincingly prove were rated incorrect given the expected correct answers.

I am not an expert on IMO. However, someone has pointed out some issues with P3 proof in the GitHub issue.

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