NikshepShetty
/

Florence-2-DOCCI-FT

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+# Florence-2 DOCCI-FT LoRA Adapter
+This repository contains a LoRA adapter trained on google/docci for the Florence-2-base-FT model. It's designed to enhance the model's captioning capabilities, providing more detailed captions.
+## Usage
+To use this LoRA adapter, you'll need to load it along with the Florence-2-base model using the PEFT library. Here's an example of how to use it:
+```python
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForCausalLM
+from peft import PeftModel, PeftConfig
+import requests
+def caption(image):
+  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)
+  prompt = "<MORE_DETAILED_CAPTION>"
+  adapter_name = "NikshepShetty/Florence-2-DOCCI-FT"
+  model = PeftModel.from_pretrained(base_model, adapter_name, trust_remote_code=True)
+  inputs = processor(text=prompt, images=image, return_tensors="pt")
+  generated_ids = model.generate(
+      input_ids=inputs["input_ids"],
+      pixel_values=inputs["pixel_values"],
+      max_new_tokens=1024,
+      do_sample=False,
+      num_beams=3
+  )
+  generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+  parsed_answer = processor.post_process_generation(generated_text, task="<MORE_DETAILED_CAPTION>", image_size=(image.width, image.height))
+  print(parsed_answer)
+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)
+caption(image)
+```
+This code demonstrates how to:
+1. Load the base Florence-2 model
+2. Load the LoRA adapter
+3. Process an image and generate a detailed caption
+Note: Make sure you have the required libraries installed: transformers, peft, einops, flash_attn, timm, Pillow, and requests.