Instructions to use OedoSoldier/ViGOS-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OedoSoldier/ViGOS-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="OedoSoldier/ViGOS-7B")
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("OedoSoldier/ViGOS-7B")
model = AutoModelForMultimodalLM.from_pretrained("OedoSoldier/ViGOS-7B", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OedoSoldier/ViGOS-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OedoSoldier/ViGOS-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OedoSoldier/ViGOS-7B",
		"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/OedoSoldier/ViGOS-7B

SGLang

How to use OedoSoldier/ViGOS-7B 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 "OedoSoldier/ViGOS-7B" \
    --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": "OedoSoldier/ViGOS-7B",
		"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 "OedoSoldier/ViGOS-7B" \
        --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": "OedoSoldier/ViGOS-7B",
		"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 OedoSoldier/ViGOS-7B with Docker Model Runner:
```
docker model run hf.co/OedoSoldier/ViGOS-7B
```

OedoSoldier commited on Jun 18

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@@ -264,16 +264,6 @@ Score / Prior, in percent:
 | ViLP-F | 62.67 / 97.00 |
 | ViLP-P | 61.67 / 91.67 |
-## Limitations
-The paper identifies several limitations that also apply to this released model:
-- The generated visual description may be incomplete or incorrect.
-- The image-only perception teacher may produce generic descriptions.
-- Training requires extra teacher forward passes.
-- PALR is a diagnostic under specific counterfactual choices and does not prove that all shortcut behavior is eliminated.
-- The model may hallucinate, misread fine visual details, fail on out-of-domain images, or inherit biases from the base model and training data.
 ## Ethical Considerations
 Users should validate the model carefully before deployment. The model can generate plausible but incorrect visual descriptions and rationales. In user-facing applications, consider presenting only concise final answers, or clearly mark generated descriptions and rationales as model-generated rather than authoritative evidence.

 | ViLP-F | 62.67 / 97.00 |
 | ViLP-P | 61.67 / 91.67 |
 ## Ethical Considerations
 Users should validate the model carefully before deployment. The model can generate plausible but incorrect visual descriptions and rationales. In user-facing applications, consider presenting only concise final answers, or clearly mark generated descriptions and rationales as model-generated rather than authoritative evidence.