Instructions to use JanHutter/verifierreward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JanHutter/verifierreward with Transformers:

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

pipe = pipeline("image-text-to-text", model="JanHutter/verifierreward")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("JanHutter/verifierreward")
model = AutoModelForMultimodalLM.from_pretrained("JanHutter/verifierreward", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JanHutter/verifierreward with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JanHutter/verifierreward"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JanHutter/verifierreward",
		"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/JanHutter/verifierreward

SGLang

How to use JanHutter/verifierreward 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 "JanHutter/verifierreward" \
    --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": "JanHutter/verifierreward",
		"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 "JanHutter/verifierreward" \
        --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": "JanHutter/verifierreward",
		"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 JanHutter/verifierreward with Docker Model Runner:
```
docker model run hf.co/JanHutter/verifierreward
```

JanHutter commited on Sep 15

Commit

fbbd639

verified ·

1 Parent(s): 58d3fb2

Update README.md

Browse files

Files changed (1) hide show

README.md +0 -7

README.md CHANGED Viewed

@@ -11,11 +11,6 @@ tags:
 # VerifierReward Qwen3-VL 8B
-This is a Qwen3-VL-8B-Instruct checkpoint fine-tuned as a yes/no image-text
-alignment verifier.
-## Load from Hugging Face
 ```python
 import torch
 from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
@@ -73,5 +68,3 @@ yes_id = yes_ids[0]
 reward = torch.softmax(logits, dim=-1)[:, yes_id]
 print(reward.item())
 ```
-For reproducible loading, pass the same commit SHA as `revision` to both `from_pretrained` calls.

 # VerifierReward Qwen3-VL 8B
 ```python
 import torch
 from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
 reward = torch.softmax(logits, dim=-1)[:, yes_id]
 print(reward.item())
 ```