Instructions to use bingyang-lei/Intern-S2-Preview-SimpleOPD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bingyang-lei/Intern-S2-Preview-SimpleOPD with Transformers:

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

pipe = pipeline("image-text-to-text", model="bingyang-lei/Intern-S2-Preview-SimpleOPD", 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 AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained("bingyang-lei/Intern-S2-Preview-SimpleOPD", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bingyang-lei/Intern-S2-Preview-SimpleOPD with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bingyang-lei/Intern-S2-Preview-SimpleOPD"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bingyang-lei/Intern-S2-Preview-SimpleOPD",
		"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/bingyang-lei/Intern-S2-Preview-SimpleOPD

SGLang

How to use bingyang-lei/Intern-S2-Preview-SimpleOPD 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 "bingyang-lei/Intern-S2-Preview-SimpleOPD" \
    --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": "bingyang-lei/Intern-S2-Preview-SimpleOPD",
		"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 "bingyang-lei/Intern-S2-Preview-SimpleOPD" \
        --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": "bingyang-lei/Intern-S2-Preview-SimpleOPD",
		"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 bingyang-lei/Intern-S2-Preview-SimpleOPD with Docker Model Runner:
```
docker model run hf.co/bingyang-lei/Intern-S2-Preview-SimpleOPD
```

Intern-S2-Preview-SimpleOPD

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---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/internlm/Intern-S2-Preview/blob/main/LICENSE
base_model: internlm/Intern-S2-Preview
pipeline_tag: image-text-to-text
---

# Intern-S2-Preview-OPD

**Intern-S2-Preview-OPD** is a post-trained version of
[Intern-S2-Preview](https://huggingface.co/internlm/Intern-S2-Preview), an efficient 35B scientific multimodal foundation model.

The model is post-trained using the method introduced in
[SimpleOPD: Simple Tokenizer-Agnostic On-Policy Distillation of Long-Context Reasoning](PAPER_LINK_PLACEHOLDER).
This post-training process substantially improves the model's reasoning performance, particularly on challenging proof and mathematical reasoning benchmarks.

## Evaluation Results

| Model | ProofBench | AnswerBench | AIME25 | AMOBench |
|---|---:|---:|---:|---:|
| Intern-S2-Preview | 21.70 | 76.03 | 88.33 | 58.00 |
| **Intern-S2-Preview-OPD** | **44.50 (+22.80)** | **80.10 (+4.07)** | **95.00 (+6.67)** | **59.50 (+1.50)** |
| [SU-01 (Teacher Model)](https://huggingface.co/Simplified-Reasoning/SU-01) | 45.00 | 77.50 | 94.60 | 61.75 |

The values in parentheses indicate absolute improvements over the base model.

## Usage

Intern-S2-Preview-OPD uses the same model architecture and inference interface as Intern-S2-Preview. Please refer to the
[Intern-S2-Preview model card](https://huggingface.co/internlm/Intern-S2-Preview)
for deployment instructions and recommended inference settings.

## License

This model is released under the
[Apache License 2.0](https://huggingface.co/internlm/Intern-S2-Preview/blob/main/LICENSE).