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

Libraries

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

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

pipe = pipeline("image-text-to-text", model="internlm/Intern-S1-Pro", 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 AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("internlm/Intern-S1-Pro", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

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

SGLang

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

Intern-S1-Pro

Commit History

Sep 15

03:08
Fix transformers>=5.10 compatibility in video_processing_interns1_pro.py (#18)⁠

lvhan

054ef1e

Mar 30

03:46
Adapt tokenization_interns1.py to transformers>=5.0.0 (#15)⁠

yehaochen +1: Zhangyc02

66f5286

Mar 27

13:18
update tech report link and citation

RangiLyu

Verified signature
f1c1974

Feb 14

11:29
[Feature] Update time series (#13)⁠

QipengGuo +1: yehaochen

619e440
11:29
Update vllm info, add time series demo (#11)⁠

QipengGuo +1: jack-zxy

e00807a

Feb 12

07:13
Update sglang info (#10)⁠

QipengGuo +1: unsubscribe

82d0f52

Feb 9

06:51
fix check_model_inputs (#6)⁠

RangiLyu +1: huang11

1ba4154
06:50
Update README.md by emphasizing the usage of the LLM inference engine. (#8)⁠

RangiLyu +1: QipengGuo

88ed679

Feb 5

02:34
Update README.md

RangiLyu

Verified signature
9f67b9f
02:33
Update generation_config.json

RangiLyu

Verified signature
fc85839

Feb 4

13:25
update figs

RangiLyu

Verified signature
41a3377
13:21
Update README.md

RangiLyu

Verified signature
f1a9d72
13:00
Update README.md

RangiLyu

Verified signature
83a1263
08:07
Update README.md

RangiLyu

Verified signature
6f8921e
08:02
Upload LICENSE.txt

RangiLyu

Verified signature
9e93e46
07:40
upload figs

RangiLyu

Verified signature
aed9aea
07:39
Create README.md

RangiLyu

Verified signature
b1af760
07:27
Create deployment_guide.md (#1)⁠

RangiLyu +1: jack-zxy

e66e0ef

Feb 2

18:25
Add files using upload-large-folder tool

haijunlv

Verified signature
80f90fc
18:20
Add files using upload-large-folder tool

haijunlv

Verified signature
0d382e3
15:05
initial commit

haijunlv

Verified signature
4b114d9

Commit History

Sep 15

Fix transformers>=5.10 compatibility in video_processing_interns1_pro.py (#18)⁠

Mar 30

Adapt tokenization_interns1.py to transformers>=5.0.0 (#15)⁠

Mar 27

update tech report link and citation

Feb 14

[Feature] Update time series (#13)⁠

Update vllm info, add time series demo (#11)⁠

Feb 12

Update sglang info (#10)⁠

Feb 9

fix check_model_inputs (#6)⁠

Update README.md by emphasizing the usage of the LLM inference engine. (#8)⁠

Feb 5

Update README.md

Update generation_config.json

Feb 4

update figs

Update README.md

Update README.md

Update README.md

Upload LICENSE.txt

upload figs

Create README.md

Create deployment_guide.md (#1)⁠

Feb 2

Add files using upload-large-folder tool

Add files using upload-large-folder tool

initial commit