Instructions to use internlm/Intern-S1-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/Intern-S1-mini with Transformers:

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

pipe = pipeline("image-text-to-text", model="internlm/Intern-S1-mini", 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)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("internlm/Intern-S1-mini", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use internlm/Intern-S1-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/Intern-S1-mini"
# 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-S1-mini",
		"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-S1-mini

SGLang

How to use internlm/Intern-S1-mini 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-S1-mini" \
    --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-S1-mini",
		"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-S1-mini" \
        --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-S1-mini",
		"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-S1-mini with Docker Model Runner:
```
docker model run hf.co/internlm/Intern-S1-mini
```

Strong, Fast Model for Laptop!

by BingoBird - opened Nov 5, 2025

Discussion

BingoBird

Nov 5, 2025

Not tried vision yet.
As a .gguf quant a Thinkpad T495 (ryzen 3500u, Vega 8) gets:

| Qwen3-8B-Intern-S1-mini.i1-Q4_K_M.gguf                          | qwen3 8B Q4_K - Medium         |   4.68 GiB |     8.20 B | Vulkan     |   0 |       5 |           tg128 |          3.58 ± 0.01 |
| Qwen3-8B-Intern-S1-mini.i1-Q4_K_M.gguf                          | qwen3 8B Q4_K - Medium         |   4.68 GiB |     8.20 B | Vulkan     |  30 |       5 |           pp512 |         12.06 ± 0.04 |
| Qwen3-8B-Intern-S1-mini.i1-Q4_K_M.gguf                          | qwen3 8B Q4_K - Medium         |   4.68 GiB |     8.20 B | Vulkan     |  30 |       5 |           tg128 |          4.22 ± 0.05 |
| Qwen3-8B-Intern-S1-mini.i1-Q4_K_M.gguf                          | qwen3 8B Q4_K - Medium         |   4.68 GiB |     8.20 B | Vulkan     |  99 |       5 |           pp512 |         26.54 ± 0.14 |
| Qwen3-8B-Intern-S1-mini.i1-Q4_K_M.gguf                          | qwen3 8B Q4_K - Medium         |   4.68 GiB |     8.20 B | Vulkan     |  99 |       5 |           tg128 |          4.47 ± 0.01 |

And leveraging Qwen3's advanced reasoning patterns, this is one of the few small models to get the 'chess puzzle' right:
Let's warm up with a logic puzzle: Alice, Bertha and Cindy are in a room cut off from the outside world. The only things in the room are a book and a chess set. Alice is reading the book. Bertha is playing a game of chess. What is Cindy doing?

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