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

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-# Intern-S2-Mobius Deployment Guide
-The Intern-S2-Mobius release is a 35B model stored in bfloat16 weight format. This guide provides deployment examples for the following configurations:
-- MTP speculative decoding (Recommended)
-- Basic serving without MTP
-> NOTE: The commands below are reference configurations. Inference frameworks are under active development, so use the latest framework documentation and your local validation results when tuning production deployments.
-## LMDeploy
-Use the latest LMDeploy with Intern-S2-Mobius support. The examples below use single-GPU serving.
-- Serving With MTP (Recommended)
-```bash
-lmdeploy serve api_server \
-    internlm/Intern-S2-Mobius \
-    --trust-remote-code \
-    --backend pytorch \
-    --tp 1 \
-    --speculative-algorithm qwen3_5_mtp \
-    --speculative-num-draft-tokens 4 \
-    --dtype bfloat16 \
-    --max-batch-size 64
-```
-- Basic Serving Without MTP
-```bash
-lmdeploy serve api_server \
-    internlm/Intern-S2-Mobius \
-    --trust-remote-code \
-    --backend pytorch \
-    --dtype bfloat16 \
-    --tp 1
-```
-## Transformers
-Use a recent Transformers version with remote-code loading enabled.
-- Basic Inference
-```python
-import torch
-from transformers import AutoModelForImageTextToText, AutoTokenizer
-model_path = "internlm/Intern-S2-Mobius"
-tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-model = AutoModelForImageTextToText.from_pretrained(
-    model_path,
-    trust_remote_code=True,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-).eval()
-messages = [
-    {"role": "user", "content": "Give me a short introduction to Intern-S2-Mobius."}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True,
-)
-inputs = tokenizer(text, return_tensors="pt").to(model.device)
-with torch.no_grad():
-    output_ids = model.generate(
-        **inputs,
-        max_new_tokens=512,
-        do_sample=True,
-        temperature=0.8,
-        top_p=1,
-    )
-response_ids = output_ids[0][inputs["input_ids"].shape[-1]:]
-print(tokenizer.decode(response_ids, skip_special_tokens=True))
-```
-## vLLM
-Use the latest vLLM Docker image or source build with Intern-S2-Mobius support.
-- Serving With MTP (Recommended)
-```bash
-vllm serve \
-    internlm/Intern-S2-Mobius \
-    --trust-remote-code \
-    --tensor-parallel-size 2 \
-    --reasoning-parser qwen3 \
-    --enable-auto-tool-choice \
-    --tool-call-parser qwen3_coder \
-    --spec-method mtp \
-    --spec-tokens 4
-```
-- Basic Serving Without MTP
-```bash
-vllm serve \
-    internlm/Intern-S2-Mobius \
-    --trust-remote-code \
-    --tensor-parallel-size 2 \
-    --reasoning-parser qwen3 \
-    --enable-auto-tool-choice \
-    --tool-call-parser qwen3_coder
-```