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

Libraries

How to use AI4SGI/ExoMind with Transformers:

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

pipe = pipeline("image-text-to-text", model="AI4SGI/ExoMind")
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")
model = AutoModelForMultimodalLM.from_pretrained("AI4SGI/ExoMind", 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 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AI4SGI/ExoMind"
# 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",
		"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

SGLang

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

ExoMind / README.md

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metadata

library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3.5-35B-A3B
base_model_relation: finetune
pipeline_tag: image-text-to-text
language:
  - en
  - zh
tags:
  - exomind
  - scientific-reasoning
  - scientific-research
  - agentic
  - tool-use
  - multimodal
  - vision-language
  - qwen3.5
  - safetensors

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 is the first extended-mind-inspired agentic system designed for scientific reasoning and research. It organizes a general-purpose model, specialized interaction objects, and autonomous interaction processes as one system, allowing the model to discover sources, ground evidence, execute verification, and update its reasoning around each scientific problem.

This repository hosts the main checkpoint, fine-tuned from Qwen3.5-35B-A3B. With training-value-aware data engineering, a scientific interaction framework, and two-stage progressive Chain-of-Interaction training, ExoMind raises the average score across eight scientific benchmarks from 36.2 to 67.5, achieves the highest average among all evaluated models, and ranks first on six benchmarks.

Highlights

Extended-mind-inspired intelligence: unifies the LLM, interaction objects, and autonomous interaction processes as a scientific agentic system.
Training-value-aware data engineering: identifies challenging, learnable problems and routes them to pure-reasoning or interaction-reasoning data.
Scientific interaction: turns source discovery, evidence grounding, executable verification, and observation integration into composable objects.
Progressive CoI training: jointly develops intrinsic reasoning and autonomous interaction using a few thousand high-quality trajectories.
Efficient frontier performance: completes two-stage full-parameter SFT in 1–2 days on 8 NVIDIA H200 GPUs while improving all six evaluated general capability benchmarks over the 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 \
  --port 8000 \
  --tensor-parallel-size 8 \
  --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 \
  --host 0.0.0.0 \
  --port 8000 \
  --tp-size 8 \
  --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",
    messages=[
        {
            "role": "user",
            "content": "Develop and verify a rigorous solution to this scientific problem: ...",
        }
    ],
    temperature=1.0,
    top_p=0.95,
    extra_body={"top_k": 20},
)
print(response.choices[0].message.content)

The complete scientific interaction workflow and prompt contracts are available in the ExoMind GitHub repository.

Evaluation

Under the technical report's evaluation setup, ExoMind reaches an eight-benchmark average of 67.5, compared with 54.2 for the next-best representative model shown below.

🥇 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

See the interactive evaluation explorer for the complete model list, benchmark scopes, settings, and rankings.

Intended Use

ExoMind is intended for research and development in scientific question answering, literature-grounded investigation, mathematical and computational reasoning, code-assisted verification, and agentic scientific workflows.

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}
}