Instructions to use beaupi/granite-vision-4.1-4b-oQ8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beaupi/granite-vision-4.1-4b-oQ8 with Transformers:

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

pipe = pipeline("image-text-to-text", model="beaupi/granite-vision-4.1-4b-oQ8", trust_remote_code=True)
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("beaupi/granite-vision-4.1-4b-oQ8", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("beaupi/granite-vision-4.1-4b-oQ8", trust_remote_code=True, 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 beaupi/granite-vision-4.1-4b-oQ8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beaupi/granite-vision-4.1-4b-oQ8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beaupi/granite-vision-4.1-4b-oQ8",
		"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/beaupi/granite-vision-4.1-4b-oQ8

SGLang

How to use beaupi/granite-vision-4.1-4b-oQ8 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 "beaupi/granite-vision-4.1-4b-oQ8" \
    --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": "beaupi/granite-vision-4.1-4b-oQ8",
		"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 "beaupi/granite-vision-4.1-4b-oQ8" \
        --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": "beaupi/granite-vision-4.1-4b-oQ8",
		"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 beaupi/granite-vision-4.1-4b-oQ8 with Docker Model Runner:
```
docker model run hf.co/beaupi/granite-vision-4.1-4b-oQ8
```

granite-vision-4.1-4b-oQ8 / configuration.py

beaupi

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	from typing import Optional
	import logging
	from transformers import LlavaNextConfig

	logger = logging.getLogger(__name__)


	class Granite4VisionConfig(LlavaNextConfig):
	model_type = "granite4_vision"
	def __init__(
	self,
	downsample_rate=None,
	use_image_newline_parameter=True,
	deepstack_layer_map: Optional[list] = None,
	use_spatial_sampling: bool = False,
	spatial_stride: int = 2,
	spatial_vision_layer: int = -1,
	spatial_target_layers: Optional[list] = None,
	projector_dropout=0.1,
	**kwargs
	):
	self.downsample_rate = downsample_rate
	self.use_image_newline_parameter = use_image_newline_parameter
	self.projector_dropout = projector_dropout

	# Deepstack layer map: list of (vision_layer_idx, llm_layer_idx) tuples.
	# Features from each vision layer are extracted, downsampled, and injected
	# at the corresponding LLM layer during forward pass.
	# e.g., [(-25, 12), (-17, 8), (-9, 4), (-1, 0)]
	if deepstack_layer_map is not None:
	self.deepstack_layer_map = [(int(v), int(l)) for v, l in deepstack_layer_map]
	assert len(self.deepstack_layer_map) == len(set(self.deepstack_layer_map)), "expecting no duplicates"
	else:
	self.deepstack_layer_map = None

	# Spatial sampling: extracts 4 groups from a single vision layer using
	# spatial offset sampling (top-left, top-right, bottom-left, bottom-right
	# of each 2x2 block), each injected at a different LLM layer.
	self.use_spatial_sampling = use_spatial_sampling
	self.spatial_stride = spatial_stride
	self.spatial_vision_layer = spatial_vision_layer
	self.spatial_target_layers = spatial_target_layers or [0, 10, 20, 30]

	super().__init__(**kwargs)