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

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	---
	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](https://huggingface.co/Qwen/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](https://huggingface.co/datasets/UKPLab/ProReviewer-Dataset)) \|
	\| Architecture \| Qwen3ForCausalLM \|
	\| Parameters \| 8B \|
	\| Precision \| bfloat16 \|

	## Usage

	### With vLLM

	```bash
	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](https://github.com/UKPLab/arxiv2026-ProReviewer):

	```python
	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

	```python
	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](https://arxiv.org/abs/2606.13349)
	- Code: [UKPLab/arxiv2026-ProReviewer](https://github.com/UKPLab/arxiv2026-ProReviewer)
	- Dataset: [UKPLab/ProReviewer-Dataset](https://huggingface.co/datasets/UKPLab/ProReviewer-Dataset)

	## Citation

	```bibtex
	@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](https://opensource.org/licenses/MIT).