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)

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

Request: GGUF / quantized weights for Intern-S1-Pro

by gileneo - opened Feb 8

Discussion

gileneo

Feb 8

Hi InternLM team & community,

Is there any plan to release GGUF or other quantized weights (INT4/INT8, AWQ, GPTQ, MLX) for Intern-S1-Pro?

Given that Intern-S1-Pro is a MoE model (~1T total params, ~22B active), a GGUF quant with proper expert routing would make it much more accessible for local inference and research, especially on large-memory systems (e.g. Mac Studio 512 GB RAM version).

Even an experimental / research-grade GGUF (or guidance on recommended quantization settings for MoE experts) would be extremely valuable for the community.

Thanks a lot for the great work on InternLM models!

JDWarner

Feb 11

Agree with quants in general, especially at 4-8 bits. However, another angle which I would love to see explored would be the use of many narrow corpuses to REAP this model by paring down to the experts which are relevant for a given domain.

Mathematics. Physics. Chemistry. Civil Engineering. Different medical subspecialties. I hypothesize that a model like this would work amazingly well as a base for an entire family of subject matter expert agents-on-demand.

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