from grid.model.perception.vlm.llava import LLaVA
car = AirGenCar()

# We will be capturing an image from the AirGen simulator 
# and run model inference on it.

img =  car.getImage("front_center", "rgb").data

model = LLaVA(use_local = False)
result = model.run(rgbimage=img, prompt=<prompt>)
print(result)

The LLaVA class provides a wrapper for the LLaVA model, which answers questions about visual media (images) using the LLaVA framework.

​
class LLaVA()
​
use_local
boolean
default:
"False"

If True, inference call is run on the local VM, else offloaded onto GRID-Cortex. Defaults to False.

​
def run()
​
rgbimage
np.ndarray
required

The input RGB image of shape (M,N,3)(M,N,3).

​
prompt
str
required

The question to answer about the media.

​
Returns
str

The response to the prompt.

from grid.model.perception.vlm.llava import LLaVA
car = AirGenCar()

# We will be capturing an image from the AirGen simulator 
# and run model inference on it.

img =  car.getImage("front_center", "rgb").data

model = LLaVA(use_local = False)
result = model.run(rgbimage=img, prompt=<prompt>)
print(result)

This code is licensed under the Apache 2.0 License.

Was this page helpful?

Previous
MiniCPM
Next