Instructions to use inclusionAI/Ring-2.5-1T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/Ring-2.5-1T with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/Ring-2.5-1T", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inclusionAI/Ring-2.5-1T", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("inclusionAI/Ring-2.5-1T", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/Ring-2.5-1T with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/Ring-2.5-1T"
# 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-2.5-1T",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/Ring-2.5-1T

SGLang

How to use inclusionAI/Ring-2.5-1T 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-2.5-1T" \
    --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-2.5-1T",
		"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-2.5-1T" \
        --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-2.5-1T",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/Ring-2.5-1T with Docker Model Runner:
```
docker model run hf.co/inclusionAI/Ring-2.5-1T
```

LiangJiang commited on Feb 13

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b3087bf

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@@ -40,7 +40,7 @@ Note: If you are interested in the previous version, please visit the past model
 ## Deep Thinking & Long-horizon task Execution
 <p align="center">
-    <img src="https://mdn.alipayobjects.com/huamei_d2byvp/afts/img/HAkCTKAY7akAAAAAVdAAAAgADod9AQFr/original />
 </p>
 For evaluating the Deep Thinking and Long-term Execution capabilities of Ring-2.5-1T, we selected representative open-source thinking models (DeepSeek-v3.2-Thinking, Kimi-K2.5-Thinking) and closed-source APIs (GPT-5.2-thinking-high, Gemini-3.0-Pro-preview-thinking-high, Claude-Opus-4.5-Extended-Thinking) as references. Ring-2.5-1T achieves state-of-the-art open-source performance across both high-difficulty reasoning tasks—including mathematics, coding, and logical reasoning (IMOAnswerBench, AIME 26, HMMT 25, LiveCodeBench, ARC-AGI-V2)—and long-horizon task execution such as agent search, tool calling, and software engineering  (Gaia2-search, Tau2-bench, and SWE-Bench Verified).

 ## Deep Thinking & Long-horizon task Execution
 <p align="center">
+    <img src="https://mdn.alipayobjects.com/huamei_d2byvp/afts/img/HAkCTKAY7akAAAAAVdAAAAgADod9AQFr/original" />
 </p>
 For evaluating the Deep Thinking and Long-term Execution capabilities of Ring-2.5-1T, we selected representative open-source thinking models (DeepSeek-v3.2-Thinking, Kimi-K2.5-Thinking) and closed-source APIs (GPT-5.2-thinking-high, Gemini-3.0-Pro-preview-thinking-high, Claude-Opus-4.5-Extended-Thinking) as references. Ring-2.5-1T achieves state-of-the-art open-source performance across both high-difficulty reasoning tasks—including mathematics, coding, and logical reasoning (IMOAnswerBench, AIME 26, HMMT 25, LiveCodeBench, ARC-AGI-V2)—and long-horizon task execution such as agent search, tool calling, and software engineering  (Gaia2-search, Tau2-bench, and SWE-Bench Verified).