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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Training Details

Training Data

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Inference Procedure


!pip install -qU transformers
!pip install -qU accelerate bitsandbytes einops flash_attn timm
!pip install -q datasets

from PIL import Image
import requests
import torch
from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig, TrainingArguments, AutoModelForCausalLM
import requests
import re
from transformers import AutoConfig, AutoProcessor, AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True,)
processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True,)
model = AutoModelForCausalLM.from_pretrained("Mit1208/Florence-2-DocLayNet", trust_remote_code=True, config = base_model.config)

def run_example(task_prompt, image, text_input=None):
    if text_input is None:
        prompt = task_prompt
    else:
        prompt = task_prompt + text_input
    print(prompt)
    inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
    generated_ids = model.generate(
      input_ids=inputs["input_ids"],
      pixel_values=inputs["pixel_values"],
      max_new_tokens=1024,
      early_stopping=False,
      do_sample=False,
      num_beams=3,
    )
    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
    print(generated_text)
    parsed_answer = processor.post_process_generation(
        generated_text,
        task=task_prompt,
        image_size=(image.width, image.height)
    )

    return parsed_answer

from PIL import Image
import requests

image = Image.open('form-1.png').convert('RGB')
task_prompt = '<OD>'
results = run_example(task_prompt, example['image'].resize(size=(1000, 1000)))
print(results)
Downloads last month
24
Safetensors
Model size
271M params
Tensor type
F32
·
Inference API
Inference API (serverless) does not yet support model repos that contain custom code.

Dataset used to train Mit1208/Florence-2-DocLayNet