Update README.md

README.md

@@ -46,6 +46,59 @@ Use the code below to get started with the model.
 
 [More Information Needed]
 
-### Training Procedure
+### Inference Procedure
+
+```python
+
+!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)
+
+```
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->