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

Libraries

How to use internlm/Intern-S1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="internlm/Intern-S1", 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", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use internlm/Intern-S1 with vLLM:

Install from pip and serve model

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

SGLang

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

jack-zxy commited on Jul 28, 2025

Commit

a61bd1e

1 Parent(s): 8aa545a

lazy import CONFIG_MAPPING

Browse files

Files changed (1) hide show

configuration_interns1.py +3 -1

configuration_interns1.py CHANGED Viewed

@@ -15,7 +15,7 @@
 from transformers.configuration_utils import PretrainedConfig
-from transformers import CONFIG_MAPPING, AutoConfig
 class InternS1VisionConfig(PretrainedConfig):
@@ -208,9 +208,11 @@ class InternS1Config(PretrainedConfig):
             self.vision_config = InternS1VisionConfig()
         if isinstance(text_config, dict):
             text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "qwen2"  # todo
             text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
         elif text_config is None:
             text_config = CONFIG_MAPPING["qwen2"]()  # todo
         self.text_config = text_config

 from transformers.configuration_utils import PretrainedConfig
+from transformers import AutoConfig
 class InternS1VisionConfig(PretrainedConfig):
             self.vision_config = InternS1VisionConfig()
         if isinstance(text_config, dict):
+            from transformers import CONFIG_MAPPING
             text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "qwen2"  # todo
             text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
         elif text_config is None:
+            from transformers import CONFIG_MAPPING
             text_config = CONFIG_MAPPING["qwen2"]()  # todo
         self.text_config = text_config