Instructions to use Jaew00Lee/Qwen3-4B-PRInTS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jaew00Lee/Qwen3-4B-PRInTS with Transformers:

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

pipe = pipeline("text-generation", model="Jaew00Lee/Qwen3-4B-PRInTS")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jaew00Lee/Qwen3-4B-PRInTS")
model = AutoModelForCausalLM.from_pretrained("Jaew00Lee/Qwen3-4B-PRInTS")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Jaew00Lee/Qwen3-4B-PRInTS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jaew00Lee/Qwen3-4B-PRInTS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jaew00Lee/Qwen3-4B-PRInTS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jaew00Lee/Qwen3-4B-PRInTS

SGLang

How to use Jaew00Lee/Qwen3-4B-PRInTS 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 "Jaew00Lee/Qwen3-4B-PRInTS" \
    --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": "Jaew00Lee/Qwen3-4B-PRInTS",
		"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 "Jaew00Lee/Qwen3-4B-PRInTS" \
        --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": "Jaew00Lee/Qwen3-4B-PRInTS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jaew00Lee/Qwen3-4B-PRInTS with Docker Model Runner:
```
docker model run hf.co/Jaew00Lee/Qwen3-4B-PRInTS
```

Model Card for PRInTS (Process Reward via Information gain scoring and Trajectory Summarization) Qwen3-4B

This repository hosts the PRInTS (Process Reward via Information gain scoring and Trajectory Summarization) Qwen3-4B model. PRInTS is a generative process reward model for long-horizon information-seeking tasks.

Model Details

Model Description

PRInTS (Process Reward via Information gain scoring and Trajectory Summary) is a generative PRM jointly trained with two key abilities for fine-grained guidance under the challenge of context accumulation.

Key Highlights:

🎯 PRInTS as a scorer: evaluates agent's multiple candidate next trajectory steps based on the summarized context and current tool response, and outputs dense scores based on the PRM's reasoning across multiple step quality dimensions (e.g., interpretation of tool outputs, tool call informativeness)
📝 PRInTS as a summarizer: recursively updates a compact information-seeking trajectory summary to keep input length bounded and preserve key information for its subsequent score evaluation.

Developed by: Jaewoo Lee, Archiki Prasad, Justin Chih-Yao Chen, Zaid Khan, Elias Stengel-Eskin, Mohit Bansal
Model type: Qwen3ForCausalLM, fine-tuned Large Language Model
Language(s) (NLP): English
License: MIT
Finetuned from model: Qwen3-4B

Model Sources

Repository: https://github.com/G-JWLee/PRInTS
Paper: PRInTS: Reward Modeling for Long-Horizon Information Seeking

Overview of PRInTS

Uses

Test-time scaling

The PRInTS (Qwen3-4B) model provides fine-grained guidance for information-seeking agents at test time, estimating step-level information-gain scores across n rollouts of the agents.

Citation

If you find this work useful, please consider citing us:

@article{lee2024prints,
      title={PRInTS: Reward Modeling for Long-Horizon Information Seeking},
      author={Jaewoo Lee and Archiki Prasad and Justin Chih-Yao Chen and Zaid Khan and Elias Stengel-Eskin and Mohit Bansal},
      year={2025},
      journal={arXiv preprint arXiv:2511.19314},
      url={https://arxiv.org/abs/2511.19314},
}