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", 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 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
```

Haixx commited on 18 days ago

Commit

4e8b619

verified ·

1 Parent(s): 0ab17cd

Update README.md

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README.md +11 -8

README.md CHANGED Viewed

@@ -45,13 +45,17 @@ ProReviewer-8B is the backbone model for the **ProReviewer** agent, an R1-style
 ## Usage
 ### 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.core.proreviewer import ProReviewer
-from reviewer.core.review_env import ReviewEnv
 from reviewer.evaluation import run_inference
 paper = {
@@ -60,15 +64,14 @@ paper = {
     "human_avg_score": 5.0,
 }
-result = await run_inference(paper, model="UKPLab/ProReviewer-8B")
-```
-### With vLLM
-```bash
-vllm serve UKPLab/ProReviewer-8B --max-model-len 32768 --dtype bfloat16
 ```
 ### With Transformers
 ```python

 ## 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 = {
     "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