sanctumoferrors/smallvisoionmodel

from transformers import AutoModelForCausalLM, AutoTokenizer from PIL import Image

model = AutoModelForCausalLM.from_pretrained( "sanctumoferrors/smallvisoionmodel", trust_remote_code=True, device_map={"": "cuda"} )

Optional, but recommended when running inference on a large number of

images since it has upfront compilation cost but significantly speeds

up inference:

model.model.compile()

Captioning

print("Short caption:") print(model.caption(image, length="short")["caption"])

print("\nNormal caption:") for t in model.caption(image, length="normal", stream=True)["caption"]: # Streaming generation example, supported for caption() and detect() print(t, end="", flush=True) print(model.caption(image, length="normal"))

Visual Querying

print("\nVisual query: 'How many people are in the image?'") print(model.query(image, "How many people are in the image?")["answer"])

Object Detection

print("\nObject detection: 'face'") objects = model.detect(image, "face")["objects"] print(f"Found {len(objects)} face(s)")

Pointing

print("\nPointing: 'person'") points = model.point(image, "person")["points"] print(f"Found {len(points)} person(s)")