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e3cb0cb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | import inference.apply_vllm_monet # the patch must be applied before importing vllm
import PIL.Image
from inference.load_and_gen_vllm import *
import os
import PIL
import re
model_path = 'Path/to/your/model'
def replace_abs_vis_token_content(s: str) -> str:
pattern = re.compile(r'(<abs_vis_token>)(.*?)(</abs_vis_token>)', flags=re.DOTALL)
return pattern.sub(r'\1<latent>\3', s)
def main():
mllm, sampling_params = vllm_mllm_init(model_path, tp=1, gpu_memory_utilization=0.8)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
conversations = [
[
{
"role": "user",
"content": [
{"type": "text", "text": "Question: Which car has the longest rental period? The choices are listed below:\n(A)DB11 COUPE.\n(B) V12 VANTAGES COUPES.\n(C) VANQUISH VOLANTE.\n(D) V12 VOLANTE.\n(E) The image does not feature the time. Put your final answer in \\boxed{}."},
{"type": "image", "image": PIL.Image.open('images/example_question.png').convert("RGB")}
]
}
]
]
inputs = vllm_mllm_process_batch_from_messages(conversations, processor)
output = vllm_generate(inputs, sampling_params, mllm)
raw_output_text = output[0].outputs[0].text
cleaned_output_text = replace_abs_vis_token_content(raw_output_text)
print(cleaned_output_text)
if __name__ == '__main__':
main()
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