Update README.md

README.md

@@ -1,3 +1,46 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+```
+pip install -q datasets flash_attn timm einops
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoProcessor, AutoConfig
+import torch
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model = AutoModelForCausalLM.from_pretrained("gokaygokay/Florence-2-SD3-Captioner", trust_remote_code=True).to(device).eval()
+processor = AutoProcessor.from_pretrained("gokaygokay/Florence-2-SD3-Captioner", trust_remote_code=True)
+
+# Function to run the model on an example
+def run_example(task_prompt, text_input, image):
+    prompt = task_prompt + text_input
+
+    # Ensure the image is in RGB mode
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    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,
+        num_beams=3
+    )
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    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
+import copy
+
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
+image = Image.open(requests.get(url, stream=True).raw)
+run_example("<DESCRIPTION>", "Describe this image in great detail.", image)
+
+# {'<DESCRIPTION>': 'Captured at eye-level on a sunny day, a light blue Volkswagen Beetle is parked on a cobblestone street. The beetle is parked in front of a yellow building with two brown doors. The door on the right side of the frame is white, while the left side is a darker shade of blue. The car is facing the camera, and the car is positioned in the middle of the street.'}
+```