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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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Direct Use
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.
1from PIL import Image
2from IPython.display import display
3import torch
4from transformers import Blip2Processor, Blip2ForConditionalGeneration
5from google.colab import files
6from peft import PeftModel
7
8# Upload image
9uploaded = files.upload()
10image_path = list(uploaded.keys())[0]
11
12# Display image
13image = Image.open(image_path).convert("RGB")
14display(image)
15
16# Load processor and model
17model_id = "efeozdilek/blip2-flan-lora-finetuned-six-epoch"
18processor = Blip2Processor.from_pretrained(model_id)
19
20# Load base BLIP-2 and apply LoRA weights
21base_model = Blip2ForConditionalGeneration.from_pretrained(
22 "Salesforce/blip2-flan-t5-xl",
23 device_map="auto",
24 torch_dtype=torch.float16
25)
26model = PeftModel.from_pretrained(base_model, model_id)
27
28# Preprocess input
29inputs = processor(images=image, return_tensors="pt")
30inputs = {k: v.to(model.device, dtype=torch.float32) for k, v in inputs.items()}
31
32# Generate report
33model.eval()
34with torch.no_grad():
35 generated_ids = model.generate(**inputs, max_new_tokens=256)
36 report = processor.tokenizer.decode(generated_ids[0], skip_special_tokens=True)
37
38print("\n📋 Generated Report:\n", report)
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Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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