from grid.model.perception.vlm.minicpm import MiniCPM
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 = MiniCPM(use_local = True)
result = model.run(media=img, prompt=<prompt>)
print(result)

The MiniCPM class provides a wrapper for MiniCPM v2.6 that answers questions based on both images and videos.

​
class MiniCPM()
​
use_local
boolean
default:
true

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

​
def run()
​
use_cortex
boolean
default:
"false"

If True, offloads inference to GRID-Cortex else runs locally on the session VM. Defaults to False.

​
fallback_to_local
boolean
default:
true

If True, falls back to local inference if cloud inference fails. Defaults to True.

​
def run()
​
media
np.ndarray

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.minicpm import MiniCPM
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 = MiniCPM(use_local = True)
result = model.run(media=img, prompt=<prompt>)
print(result)

This code is licensed under the Apache 2.0 License. We have obtained official license from the company to offer this model on GRID.

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