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

Libraries

How to use UKPLab/ProReviewer-8B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("UKPLab/ProReviewer-8B")
model = AutoModelForCausalLM.from_pretrained("UKPLab/ProReviewer-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 Settings

vLLM

How to use UKPLab/ProReviewer-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UKPLab/ProReviewer-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": "UKPLab/ProReviewer-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UKPLab/ProReviewer-8B

SGLang

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

Docker Model Runner
How to use UKPLab/ProReviewer-8B with Docker Model Runner:
```
docker model run hf.co/UKPLab/ProReviewer-8B
```

ProReviewer-8B / README.md

Haixx

Update README.md

4e8b619 verified about 23 hours ago

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metadata

library_name: transformers
base_model: Qwen/Qwen3-8B
model_type: qwen3
pipeline_tag: text-generation
license: mit
language:
  - en
tags:
  - peer-review
  - scientific-papers
  - GRPO
  - reinforcement-learning
  - paper-review
datasets:
  - UKPLab/ProReviewer-Dataset
citation: |
  @article{fang2026passive,
    title={From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent},
    author={Fang, Haishuo and Feng, Yue and Gurevych, Iryna},
    journal={arXiv preprint arXiv:2606.13349},
    year={2026}
  }

ProReviewer-8B

An RL-trained scientific peer review model based on Qwen3-8B. ProReviewer-8B is fine-tuned using Group Relative Policy Optimization (GRPO) to produce high-quality, evidence-based peer reviews of scientific papers.

Model Description

ProReviewer-8B is the backbone model for the ProReviewer agent, an R1-style reasoning agent that reviews scientific papers through structured investigation rather than passive generation. The model was trained with a multi-stage curriculum:

Training Details

Parameter	Value
Base model	Qwen/Qwen3-8B
Training method	SFT+ GRPO with step-level advantages
Training data	ICLR 2025 papers (UKPLab/ProReviewer-Dataset)
Architecture	Qwen3ForCausalLM
Parameters	8B
Precision	bfloat16

Usage

With vLLM

vllm serve UKPLab/ProReviewer-8B --max-model-len 32768 --dtype bfloat16

With the ProReviewer Agent

The recommended way to use this model is through the ProReviewer agent framework in the ProReviewer:

from reviewer.evaluation import run_inference

paper = {
    "paper_id": "example",
    "paper_content": "# Paper Title\n\nAbstract: ...",
    "human_avg_score": 5.0,
}

# Option 1: Use a config name from config.toml (model served via vLLM)
result = await run_inference(paper, model="proreviewer-8B")

# Option 2: Use a local path (loads model directly via vLLM)
result = await run_inference(paper, model="/path/to/ProReviewer-8B")

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("UKPLab/ProReviewer-8B", torch_dtype="bfloat16")
tokenizer = AutoTokenizer.from_pretrained("UKPLab/ProReviewer-8B")

Associated Resources

Paper: From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent
Code: UKPLab/arxiv2026-ProReviewer
Dataset: UKPLab/ProReviewer-Dataset

Citation

@article{fang2026passive,
  title={From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent},
  author={Fang, Haishuo and Feng, Yue and Gurevych, Iryna},
  journal={arXiv preprint arXiv:2606.13349},
  year={2026}
}

License

This model is released under the MIT License.