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
```

Incorrectly tokenizes bracketed inorganic SMILES (e.g., [O-2].[Zn-].[Zn-])

by MianzhiPan - opened Nov 16, 2025

Discussion

MianzhiPan

Nov 16, 2025

I found that the Intern-S1 tokenizer breaks inorganic SMILES that contain fully bracketed ions.

Example:

[O-2].[Zn-].[Zn-]

Tokenizer output:

['[O', '-', '2', ']', '.[', 'Z', 'n', '-]', '.[', 'Z', 'n', '-]']

Issues:

multi-character atom symbols inside brackets (Zn) are split
bracketed ions ([O-2], [Zn-]) are not treated as atomic units
symbols like .[ appear due to incorrect merging

In contrast, the tokenizer handles organic SMILES correctly:

O=C([O-])c1ccc(C(=O)[O-])c2c1CC2
→ ['O=C(', '[O-]', ')c', '1', ...]

How to treat bracketed ions (e.g., [Zn-], [O-2], [Fe+2], etc.) as single tokens the same way [O-] is handled?

Zhangyc02

Nov 18, 2025

The SMILES tokenization is trained on relevant corpora to combine words based on word frequency rather than to guarantee segmentation according to atomic units or ions. In fact, the vocabulary only contains "[O-]" and not "[Zn-]", the latter of which would be split into combinations of multiple lexical units. However, this is how it was trained, so the model will automatically "recognize" these combinations.

Therefore, the answer is that model cannot distinguish ions like [Zn-], and the best course of action is to leave it as is :)

MianzhiPan changed discussion status to closed Dec 7, 2025

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