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---
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
library_name: transformers
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Anukul
- **Finetuned from model :** unsloth/Llama-3.2-11B-Vision-Instruct
- **Dataset :** Padchest sample100k
# Model Overview
The model is designed to assist in interpreting radiology images such as X-rays, CT scans, and MRIs.
It can also provide preliminary disease identification to support medical professionals.
This fine-tune the unsloth/Llama-3.2-11B-Vision-Instruct model for a radiology image captioning task.
The model has been optimized to be twice as fast as the previous version, allowing for efficient fine-tuning.
## Dataset Description
The dataset used for this project is Padchest,It includes: Train Set, Test Set This dataset represents 100k of the original dataset
## Usage
```python
from unsloth import FastVisionModel
from PIL import Image
import numpy as np
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load model and tokenizer
model, tokenizer = FastVisionModel.from_pretrained(
"0llheaven/Llama-3.2-CXR-Instruct-V1",
load_in_4bit=True,
use_gradient_checkpointing="unsloth",
)
FastVisionModel.for_inference(model)
model.to(device)
def predict_radiology_description(image, instruction):
try:
messages = [{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": instruction}
]}]
input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
inputs = tokenizer(
image,
input_text,
add_special_tokens=False,
return_tensors="pt",
).to(device)
output_ids = model.generate(
**inputs,
max_new_tokens=256,
temperature=1.5,
min_p=0.1
)
generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
return generated_text.replace("assistant", "\n\nassistant").strip()
except Exception as e:
return f"Error: {str(e)}"
# Example of usage!
image_path = 'example_image.jpeg'
instruction = 'You are an expert radiographer. Describe accurately what you see in this image.'
image = Image.open(image_path).convert("RGB")
output = predict_radiology_description(image, instruction)
print(output)
``` |