Instructions to use purewhite42/DExplorer-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use purewhite42/DExplorer-8B with Transformers:

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

pipe = pipeline("text-generation", model="purewhite42/DExplorer-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("purewhite42/DExplorer-8B")
model = AutoModelForCausalLM.from_pretrained("purewhite42/DExplorer-8B")
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

vLLM

How to use purewhite42/DExplorer-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "purewhite42/DExplorer-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "purewhite42/DExplorer-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/purewhite42/DExplorer-8B

SGLang

How to use purewhite42/DExplorer-8B 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 "purewhite42/DExplorer-8B" \
    --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": "purewhite42/DExplorer-8B",
		"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 "purewhite42/DExplorer-8B" \
        --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": "purewhite42/DExplorer-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use purewhite42/DExplorer-8B with Docker Model Runner:
```
docker model run hf.co/purewhite42/DExplorer-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

[ICLR'26] Let's Explore Step by Step: Generating Provable Formal Statements with Deductive Exploration

Qi Liu, Kangjie Bao, Yue Yang, Xinhao Zheng, Renqiu Xia, Qinxiang Cao, Junchi Yan* (* indicates corresponding author)

School of Computer Science & School of Artificial Intelligence, Shanghai Jiao Tong University

Shanghai Innovation Institute

Please refer to the 📺GitHub repo and 📃Paper for more details.

🔍 About

DExplorer-8B is a Lean 4-based agent that generates provable formal mathematical statements through step-by-step Deductive Exploration (DExploration). Instead of directly synthesizing problems in one shot, DExplorer explores the mathematical world step by step — introducing variables/hypotheses, deriving intermediate facts, and submitting conclusions — with each step verified by the Lean 4 kernel. This ensures the provability of generated statements while enabling the synthesis of complex problems that push the limits of state-of-the-art provers.

This model is fine-tuned from Goedel-Prover-V2-8B on DExploration-40K using supervised fine-tuning.

⚙️ Usage

This model is SFTed for the DExploration task: given the current exploration state (introduced variables/hypotheses and Lean 4 context), the model proposes the next exploration step — either introducing a new variable/hypothesis, deriving a new fact, or submitting a conclusion.

See the 📺GitHub repo for prompt templates and detailed usage.

📄 Citation

If you find our work useful in your research, please cite:

@inproceedings{
liu2026lets,
title={Let's Explore Step by Step: Generating Provable Formal Statements with Deductive Exploration},
author={Qi Liu and Kangjie Bao and Yue Yang and Xinhao Zheng and Renqiu Xia and Qinxiang Cao and Junchi Yan},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=Njrkeo3DiJ}
}