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

Libraries

How to use Verus-Team/Verus-Insight-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Verus-Team/Verus-Insight-8B")
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("Verus-Team/Verus-Insight-8B")
model = AutoModelForMultimodalLM.from_pretrained("Verus-Team/Verus-Insight-8B", 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 Verus-Team/Verus-Insight-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Verus-Team/Verus-Insight-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": "Verus-Team/Verus-Insight-8B",
		"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/Verus-Team/Verus-Insight-8B

SGLang

How to use Verus-Team/Verus-Insight-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 "Verus-Team/Verus-Insight-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": "Verus-Team/Verus-Insight-8B",
		"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 "Verus-Team/Verus-Insight-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": "Verus-Team/Verus-Insight-8B",
		"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 Verus-Team/Verus-Insight-8B with Docker Model Runner:
```
docker model run hf.co/Verus-Team/Verus-Insight-8B
```

Verus-Insight-8B

Verus-Insight-8B is a vision-language model from Verus-Team for inspecting visual artifacts in videos. It produces text responses from visual inputs and instructions.

Model: Verus-Team/Verus-Insight-8B
Code, prompts and environment: Verus-Team/Verus-Insight
Benchmark: Verus-Team/Verus-Bench

Model files

Architecture: Qwen3VLForConditionalGeneration, based on Qwen3-VL-8B-Instruct.
Weight precision: BF16.
Format: four Safetensors shards with a weight index.
Tokenizer, image and video processors, and chat templates are included.

Loading

Install the environment described in the code repository. Native inference has been verified with Transformers 5.12.1 and PyTorch 2.11.0 on an NVIDIA H100 80GB GPU. See the code repository for the separate vLLM environment.

from transformers import AutoProcessor, Qwen3VLForConditionalGeneration

model_id = "Verus-Team/Verus-Insight-8B"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3VLForConditionalGeneration.from_pretrained(
    model_id,
    dtype="bfloat16",
    attn_implementation="sdpa",
).to("cuda:0").eval()

Weights occupy approximately 17.53 GB; inference also needs memory for visual inputs, activations and the key-value cache. The code repository provides inference instructions and task prompts. The chat template in this repository defines message formatting.

Limitations

Outputs can miss visual artifacts or report artifacts that are absent. Results depend on prompts, frame sampling and image resolution. When using Verus-Bench, follow its documented input ordering and timestamps.

License

Verus-Insight is provided for non-commercial research and evaluation only under the Verus license. Upstream Qwen3-VL components retain their Apache License 2.0. See NOTICE for attribution and the scope of the Verus modifications.

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Safetensors

Model size

9B params

Tensor type

BF16

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