kosmos-2.5-chat

import re
import torch
import requests
from PIL import Image, ImageDraw
from transformers import AutoProcessor, Kosmos2_5ForConditionalGeneration

repo = "microsoft/kosmos-2.5-chat"
device = "cuda:0"
dtype = torch.bfloat16

model = Kosmos2_5ForConditionalGeneration.from_pretrained(repo,
                                                          device_map=device,
                                                          torch_dtype=dtype,
                                                          attn_implementation="flash_attention_2")
processor = AutoProcessor.from_pretrained(repo)

# sample image
url = "https://huggingface.co/microsoft/kosmos-2.5/resolve/main/receipt_00008.png"

image = Image.open(requests.get(url, stream=True).raw)

question = "What is the sub total of the receipt?"
template = "<md>A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {} ASSISTANT:"
prompt = template.format(question)
inputs = processor(text=prompt, images=image, return_tensors="pt")

height, width = inputs.pop("height"), inputs.pop("width")
raw_width, raw_height = image.size
scale_height = raw_height / height
scale_width = raw_width / width

inputs = {k: v.to(device) if v is not None else None for k, v in inputs.items()}
inputs["flattened_patches"] = inputs["flattened_patches"].to(dtype)
generated_ids = model.generate(
    **inputs,
    max_new_tokens=1024,
)

generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(generated_text[0])

@article{lv2023kosmos,
  title={Kosmos-2.5: A multimodal literate model},
  author={Lv, Tengchao and Huang, Yupan and Chen, Jingye and Cui, Lei and Ma, Shuming and Chang, Yaoyao and Huang, Shaohan and Wang, Wenhui and Dong, Li and Luo, Weiyao and others},
  journal={arXiv preprint arXiv:2309.11419},
  year={2023}
}