Instructions to use internlm/Intern-S2-Mobius with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/Intern-S2-Mobius with Transformers:

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

pipe = pipeline("image-text-to-text", model="internlm/Intern-S2-Mobius", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained("internlm/Intern-S2-Mobius", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use internlm/Intern-S2-Mobius with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/Intern-S2-Mobius"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/Intern-S2-Mobius",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/internlm/Intern-S2-Mobius

SGLang

How to use internlm/Intern-S2-Mobius 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 "internlm/Intern-S2-Mobius" \
    --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": "internlm/Intern-S2-Mobius",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "internlm/Intern-S2-Mobius" \
        --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": "internlm/Intern-S2-Mobius",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use internlm/Intern-S2-Mobius with Docker Model Runner:
```
docker model run hf.co/internlm/Intern-S2-Mobius
```

Messi-Hua commited on Aug 5

Commit

fe6e6ff

verified ·

1 Parent(s): d7f1d4c

Update README

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -23,7 +23,7 @@ https://github.com/user-attachments/assets/b1970bdb-44ca-4e48-b213-fa693aa6f6fe
 ## Introduction
-We introduce **Intern-S2-Mobius**, a 35B foundation model built on the Mobius-v0 architecture, which decouples knowledge vectors from reasoning operators inside the base model. Instead of binding knowledge storage and reasoning computation layer by layer as in conventional Transformer models, Mobius organizes knowledge into a globally shared **Memory** and lets multiple **Reasoners** iteratively query and refine hidden states against this shared repository.
 This knowledge-reasoning separation gives Intern-S2-Mobius two native capabilities: **Backward Residual Connection**, where reasoning stages can access knowledge beyond their local layer hierarchy, and **Dynamic Latent Reasoning**, where deliberation, refinement, and multi-token prediction are internalized into high-density continuous states. Continual-pretrained from Qwen3.5-35B and further post-trained with SFT and RL, Intern-S2-Mobius preserves strong downstream capability while achieving substantially higher end-to-end inference efficiency, with nearly 4x speedup reported in the technical report.

 ## Introduction
+We introduce **Intern-S2-Mobius**, a 35B foundation model built on the Mobius-v0 architecture realized by Xtuner and LMDeploy. Instead of binding knowledge storage and reasoning computation layer by layer as in conventional Transformer models, Mobius organizes knowledge into a globally shared **Memory** and lets multiple **Reasoners** iteratively query and refine hidden states against this shared repository.
 This knowledge-reasoning separation gives Intern-S2-Mobius two native capabilities: **Backward Residual Connection**, where reasoning stages can access knowledge beyond their local layer hierarchy, and **Dynamic Latent Reasoning**, where deliberation, refinement, and multi-token prediction are internalized into high-density continuous states. Continual-pretrained from Qwen3.5-35B and further post-trained with SFT and RL, Intern-S2-Mobius preserves strong downstream capability while achieving substantially higher end-to-end inference efficiency, with nearly 4x speedup reported in the technical report.