Instructions to use AI4SGI/ExoMind-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AI4SGI/ExoMind-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="AI4SGI/ExoMind-9B")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("AI4SGI/ExoMind-9B")
model = AutoModelForMultimodalLM.from_pretrained("AI4SGI/ExoMind-9B", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AI4SGI/ExoMind-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AI4SGI/ExoMind-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AI4SGI/ExoMind-9B",
		"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/AI4SGI/ExoMind-9B

SGLang

How to use AI4SGI/ExoMind-9B 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 "AI4SGI/ExoMind-9B" \
    --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": "AI4SGI/ExoMind-9B",
		"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 "AI4SGI/ExoMind-9B" \
        --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": "AI4SGI/ExoMind-9B",
		"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 AI4SGI/ExoMind-9B with Docker Model Runner:
```
docker model run hf.co/AI4SGI/ExoMind-9B
```

ExoMind-9B

A compact checkpoint from ExoMind: Democratizing Scientific Intelligence via Extended-Mind-Inspired Agentic System

ExoMind Team · Shanghai Artificial Intelligence Laboratory

🔥 News

2026-08-12: 🔥 We release the ExoMind technical report, official project page, and public repository.

Overview

ExoMind-9B is the compact ExoMind checkpoint, fine-tuned from Qwen3.5-9B for lower-resource experimentation in scientific reasoning and agentic research. It follows the same extended-mind-inspired approach, organizing the model, specialized interaction objects, and autonomous interaction processes as one system.

Highlights

Compact scientific checkpoint: supports resource-conscious experiments with the ExoMind reasoning and interaction paradigm.
Scientific interaction: works with source discovery, evidence grounding, executable verification, and observation integration workflows.
Progressive CoI training: develops intrinsic reasoning and interaction behavior from selected pure-reasoning and interaction trajectories.
Multimodal foundation: retains the image-text capabilities of its Qwen3.5 base model.

Quick Start

Use a recent vLLM or SGLang release with Qwen3.5 support. The examples below use the checkpoint's configured maximum context length of 262,144 tokens.

vLLM

vllm serve AI4SGI/ExoMind-9B \
  --port 8000 \
  --tensor-parallel-size 1 \
  --max-model-len 262144 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder

SGLang

python -m sglang.launch_server \
  --model-path AI4SGI/ExoMind-9B \
  --host 0.0.0.0 \
  --port 8000 \
  --tp-size 1 \
  --context-length 262144 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder

OpenAI-Compatible API

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="AI4SGI/ExoMind-9B",
    messages=[
        {
            "role": "user",
            "content": "Develop a testable hypothesis and a rigorous verification plan for: ...",
        }
    ],
    temperature=1.0,
    top_p=0.95,
    extra_body={"top_k": 20},
)
print(response.choices[0].message.content)

Evaluation

The table below reports the main ExoMind 35B-A3B system. ExoMind-9B is provided as a compact checkpoint and has not been assigned these scores.

🥇 Best score among the representative models shown

Benchmark	⭐ Ours	Representative frontier models
Benchmark	ExoMind 35B-A3B	Claude-Opus-4.8 Thinking	GPT-5.5 (xhigh)	Gemini-3.1-Pro Preview	Kimi-K3	Qwen3.7-Max	GLM-5.2	DeepSeek-V4-Pro (Max)
🧪 Scientific Research
HLE w/ tools	50.9	🥇 57.9	52.2	51.4	56.0	53.5	54.7	48.2
FrontierScience-Research	🥇 70.0	26.7	26.7	11.7	21.7	10.0	15.0	13.3
CMT-Benchmark	🥇 84.0	46.0	43.0	43.0	34.0	34.0	20.0	28.0
CritPt	25.7	20.9	🥇 27.1	17.7	23.4	13.4	20.9	7.1
🧠 Scientific Reasoning
AMO-Bench	🥇 78.0	74.0	70.0	63.1	64.0	57.4	54.0	68.0
IMO-AnswerBench	🥇 92.8	86.8	83.8	90.0	82.8	90.0	91.0	89.8
HiPhO	🥇 49.7	46.4	43.3	43.4	42.4	38.8	37.4	38.7
FrontierScience-Olympiad	🥇 89.0	75.0	78.0	77.0	69.0	80.0	76.5	76.0
Eight-benchmark average	🥇 67.5	54.2	53.0	49.7	49.2	47.1	46.2	46.1

Complete settings and comparisons are available in the evaluation explorer.

Intended Use

ExoMind-9B is intended for scientific question answering, mathematical and computational reasoning, tool-use experiments, code-assisted verification, and resource-conscious agentic prototypes.

License and Attribution

The distributed checkpoint and upstream Qwen3.5 materials are provided under the Apache License 2.0 included in this repository. The technical report, scientific figures and results, and ExoMind brand assets are subject to the ExoMind Research Content and Brand Terms. See NOTICE.md for third-party notices.

Citation

@misc{exomind2026,
  title  = {ExoMind: Democratizing Scientific Intelligence via Extended-Mind-Inspired Agentic System},
  author = {Peng Ye and Zhuo Liu and Jingqi Ye and Fangchen Yu and Shengji Tang and Yichen Jiang and Haonan He and Zongsheng Cao and Tao Chen and Bo Zhang and Wanli Ouyang and Bowen Zhou and Lei Bai},
  year   = {2026},
  note   = {Technical report},
  url    = {https://github.com/AI4SGI/ExoMind/blob/main/Paper.pdf}
}

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