blip2-flan-lora-finetuned-six-epoch

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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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This code demonstrates how to use the fine-tuned BLIP-2 model with LoRA adapters to generate chest X-ray reports from medical images. It loads the base BLIP-2 model, applies the LoRA weights from this repository, and performs inference on an uploaded image in a Colab environment.

from PIL import Image
from IPython.display import display
import torch
from transformers import Blip2Processor, Blip2ForConditionalGeneration
from google.colab import files
from peft import PeftModel

# Upload image
uploaded = files.upload()
image_path = list(uploaded.keys())[0]

# Display image
image = Image.open(image_path).convert("RGB")
display(image)

# Load processor and model
model_id = "efeozdilek/blip2-flan-lora-finetuned-six-epoch"
processor = Blip2Processor.from_pretrained(model_id)

# Load base BLIP-2 and apply LoRA weights
base_model = Blip2ForConditionalGeneration.from_pretrained(
    "Salesforce/blip2-flan-t5-xl",
    device_map="auto",
    torch_dtype=torch.float16
)
model = PeftModel.from_pretrained(base_model, model_id)

# Preprocess input
inputs = processor(images=image, return_tensors="pt")
inputs = {k: v.to(model.device, dtype=torch.float32) for k, v in inputs.items()}

# Generate report
model.eval()
with torch.no_grad():
    generated_ids = model.generate(**inputs, max_new_tokens=256)
    report = processor.tokenizer.decode(generated_ids[0], skip_special_tokens=True)

print("\n📋 Generated Report:\n", report)

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