Instructions to use lightonai/LightOn-rerank-PW-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lightonai/LightOn-rerank-PW-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="lightonai/LightOn-rerank-PW-4B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("lightonai/LightOn-rerank-PW-4B")
model = AutoModelForMultimodalLM.from_pretrained("lightonai/LightOn-rerank-PW-4B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

sentence-transformers

How to use lightonai/LightOn-rerank-PW-4B with sentence-transformers:

from sentence_transformers import CrossEncoder

model = CrossEncoder("lightonai/LightOn-rerank-PW-4B")

query = "Which planet is known as the Red Planet?"
passages = [
	"Venus is often called Earth's twin because of its similar size and proximity.",
	"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
	"Jupiter, the largest planet in our solar system, has a prominent red spot.",
	"Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
]

scores = model.predict([(query, passage) for passage in passages])
print(scores)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lightonai/LightOn-rerank-PW-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lightonai/LightOn-rerank-PW-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lightonai/LightOn-rerank-PW-4B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/lightonai/LightOn-rerank-PW-4B

SGLang

How to use lightonai/LightOn-rerank-PW-4B 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 "lightonai/LightOn-rerank-PW-4B" \
    --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": "lightonai/LightOn-rerank-PW-4B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "lightonai/LightOn-rerank-PW-4B" \
        --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": "lightonai/LightOn-rerank-PW-4B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use lightonai/LightOn-rerank-PW-4B with Docker Model Runner:
```
docker model run hf.co/lightonai/LightOn-rerank-PW-4B
```

ameliechatelain commited on 7 days ago

Commit

8711f8f

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@@ -238,4 +238,15 @@ Full-page document images can exceed 8k tokens, so keep `--max-model-len` at 163
 | [LightOn-rerank-PW-4B](https://huggingface.co/lightonai/LightOn-rerank-PW-4B) | Qwen3.5-4B | pointwise | 59.80 |
 | [LightOn-rerank-LW-4B](https://huggingface.co/lightonai/LightOn-rerank-LW-4B) | Qwen3.5-4B | listwise | **64.69** |
-Rule of thumb: **LW** models are stronger at every size (and the gap grows with size); **PW** models are cheaper to serve and score candidates independently. For the best quality pick [LW-4B](https://huggingface.co/lightonai/LightOn-rerank-LW-4B); for the best quality/cost trade-off pick [LW-2B](https://huggingface.co/lightonai/LightOn-rerank-LW-2B); for maximum throughput on text-heavy workloads pick a PW model.

 | [LightOn-rerank-PW-4B](https://huggingface.co/lightonai/LightOn-rerank-PW-4B) | Qwen3.5-4B | pointwise | 59.80 |
 | [LightOn-rerank-LW-4B](https://huggingface.co/lightonai/LightOn-rerank-LW-4B) | Qwen3.5-4B | listwise | **64.69** |
+Rule of thumb: **LW** models are stronger at every size (and the gap grows with size); **PW** models are cheaper to serve and score candidates independently. For the best quality pick [LW-4B](https://huggingface.co/lightonai/LightOn-rerank-LW-4B); for the best quality/cost trade-off pick [LW-2B](https://huggingface.co/lightonai/LightOn-rerank-LW-2B); for maximum throughput on text-heavy workloads pick a PW model.
+## Citation
+```
+@misc{ananya2026lightonrerank,
+  title={One Adapter, Both Modalities: Field Notes from Building and Serving a Multimodal Reranker},
+  author={Ananya, Ishrat Jahan and Chatelain, Amelie},
+  year={2026},
+  howpublished={\url{https://huggingface.co/blog/lightonai/lighton-rerank}},
+}
+```