Usage Example
import requests
from PIL import Image
from transformers import MllamaForConditionalGeneration, AutoProcessor, BitsAndBytesConfig
def get_image_description(model, processor, image, initial_prompt='', max_new_tokens=70, *args, **kwargs):
initial_prompt = initial_prompt if initial_prompt != '' else "How would you describe the contents of this photo?"
messages = [
{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": initial_prompt}
]}
]
input_text = processor.apply_chat_template(
messages, add_generation_prompt=True)
inputs = processor(
image,
input_text,
add_special_tokens=False,
return_tensors="pt"
).to(model.device)
output = model.generate(**inputs, max_new_tokens=max_new_tokens)
return processor.decode(output[0])
def load_model(model_id="belkhir-nacim/l32vision_instruct"):
bnb_config = BitsAndBytesConfig(
load_in_4bit=True, # Enable 4-bit quantization
)
model = MllamaForConditionalGeneration.from_pretrained(
model_id, device_map="auto",quantization_config=bnb_config)
processor = AutoProcessor.from_pretrained(model_id)
return model, processor
model, processor = load_model()
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"
image = Image.open(requests.get(url, stream=True).raw)
result = get_image_description(
model, processor, image, initial_prompt="Tell me what do you see in the image. use keywords to describe")
print(result)
