Instructions to use SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507 with Transformers:

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

pipe = pipeline("text-generation", model="SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507")
model = AutoModelForCausalLM.from_pretrained("SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507

SGLang

How to use SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507 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 "SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507" \
    --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": "SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507" \
        --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": "SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507 with Docker Model Runner:
```
docker model run hf.co/SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507
```

ShunchiZhang commited on May 22

Commit

848c260

verified ·

1 Parent(s): 1d15c71

Add model card README

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README.md +49 -0

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	@@ -0,0 +1,49 @@

+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B-Instruct-2507
+datasets:
+  - SCAI-JHU/MindZero
+  - Chuanyang-Jin/MMToM-QA
+library_name: transformers
+pipeline_tag: text-generation
+language:
+  - en
+tags:
+  - mindzero
+  - theory-of-mind
+  - reinforcement-learning
+  - reasoning
+  - household
+---
+# MindZero-hh-tom-Qwen3-4B-Instruct-2507
+A [**MindZero**](https://scai.cs.jhu.edu/MindZero/) checkpoint trained from [`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) with self-supervised reinforcement learning for online Theory-of-Mind reasoning in household environments.
+[![Project Page](https://img.shields.io/badge/Homepage-Visit-blue?labelColor=gray&logo=homeassistantcommunitystore&logoColor=367BAF&style=flat-square)](https://scai.cs.jhu.edu/MindZero/)
+[![Collection](https://img.shields.io/badge/HuggingFace-Collection-yellow?labelColor=gray&logo=huggingface&style=flat-square)](https://huggingface.co/collections/SCAI-JHU/mindzero/)
+[![Code](https://img.shields.io/badge/GitHub-Code-black?labelColor=gray&logo=github&style=flat-square)](https://github.com/SCAI-JHU/MindZero)
+[![Paper](https://img.shields.io/badge/Paper-PDF-red?labelColor=gray&logo=arxiv&logoColor=brown&style=flat-square)](https://arxiv.org)
+## TL;DR
+**MindZero** trains (M)LLMs to perform efficient and robust online mental reasoning **without any mental-state annotations**. During training, the model is rewarded for generating mental-state hypotheses that maximize the likelihood of observed actions, as estimated by a planner — analogous to model-based ToM reasoning. After training, MindZero internalizes this reasoning into fast single-pass inference.
+## Evaluation
+| Base model                           | Checkpoint                                                                                                       | MMToM-QA |
+| ------------------------------------ | ---------------------------------------------------------------------------------------------------------------- | -------- |
+| [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)           | [MindZero-hh-tom-Qwen3-4B-Instruct-2507](https://huggingface.co/SCAI-JHU/MindZero-hh-tom-Qwen3-4B-Instruct-2507) | 72.7     |
+| [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) | [MindZero-hh-tom-Llama-3.2-3B-Instruct](https://huggingface.co/SCAI-JHU/MindZero-hh-tom-Llama-3.2-3B-Instruct)   | 77.8     |
+| [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) | [MindZero-hh-tom-Llama-3.1-8B-Instruct](https://huggingface.co/SCAI-JHU/MindZero-hh-tom-Llama-3.1-8B-Instruct)   | 76.2     |
+## Citation
+```bibtex
+@inproceedings{zhang2026mindzero,
+  title     = {MindZero: Learning Online Mental Reasoning With Zero Annotations},
+  author    = {Shunchi Zhang and Jin Lu and Chuanyang Jin and Yichao Zhou and Zhining Zhang and Tianmin Shu},
+  booktitle = {Proceedings of the 43st International Conference on Machine Learning (ICML)},
+  year      = {2026}
+}
+```