Instructions to use internlm/Intern-S2-Preview-397B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/Intern-S2-Preview-397B with Transformers:

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

pipe = pipeline("image-text-to-text", model="internlm/Intern-S2-Preview-397B", 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 AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained("internlm/Intern-S2-Preview-397B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use internlm/Intern-S2-Preview-397B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/Intern-S2-Preview-397B"
# 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-S2-Preview-397B",
		"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-S2-Preview-397B

SGLang

How to use internlm/Intern-S2-Preview-397B 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-S2-Preview-397B" \
    --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-S2-Preview-397B",
		"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-S2-Preview-397B" \
        --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-S2-Preview-397B",
		"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-S2-Preview-397B with Docker Model Runner:
```
docker model run hf.co/internlm/Intern-S2-Preview-397B
```

Add Memory Decoder to the model card

by Jiarui-Wang - opened 17 days ago

base: refs/heads/main

←

from: refs/pr/8

Discussion Files changed

-1

Files changed (1) hide show

README.md +3 -1

README.md CHANGED Viewed

@@ -12,7 +12,7 @@ pipeline_tag: image-text-to-text
   <div>&nbsp;</div>
-[💻Github Repo](https://github.com/InternLM/Intern-S1) • [🤗HF Model Collections](https://huggingface.co/collections/internlm/intern-s2) • [🤖ModelScope Collections](https://modelscope.cn/collections/Shanghai_AI_Laboratory/intern-s2) • [💬Online Chat](https://chat.intern-ai.org.cn/)
 </div>
@@ -34,6 +34,8 @@ We introduce **Intern-S2-Preview-397B**, our most capable multimodal foundation
 - **General & Scientific Long-Horizon Agents.** By connecting multiple agent frameworks to large-scale sandboxed environments for black-box agentic reinforcement learning, Intern-S2-Preview-397B improves generalization and raises the capability ceiling for long-horizon tasks in both general and scientific domains.
 ### Performance
 We evaluate the Intern-S2-Preview-397B on various benchmarks, including general datasets and scientific datasets. We report the performance comparison with the recent VLMs and LLMs below.

   <div>&nbsp;</div>
+[💻Github Repo](https://github.com/InternLM/Intern-S1) • [🤗HF Model Collections](https://huggingface.co/collections/internlm/intern-s2) • [🤖ModelScope Collections](https://modelscope.cn/collections/Shanghai_AI_Laboratory/intern-s2) • [🧩Memory Decoder](https://huggingface.co/internlm/Intern-MemDec-4B) • [💬Online Chat](https://chat.intern-ai.org.cn/)
 </div>
 - **General & Scientific Long-Horizon Agents.** By connecting multiple agent frameworks to large-scale sandboxed environments for black-box agentic reinforcement learning, Intern-S2-Preview-397B improves generalization and raises the capability ceiling for long-horizon tasks in both general and scientific domains.
+- **Continual Scientific Specialization.** Intern-S2-Preview-397B introduces Memory Decoder, a modular extension that attaches independently trained parametric memories to the frozen backbone. It provides a continual path for incorporating knowledge and specialized capabilities from emerging scientific domains without rewriting the backbone parameters. See [Intern-MemDec-4B](https://huggingface.co/internlm/Intern-MemDec-4B) for the released model and deployment instructions.
 ### Performance
 We evaluate the Intern-S2-Preview-397B on various benchmarks, including general datasets and scientific datasets. We report the performance comparison with the recent VLMs and LLMs below.