| |
| from transformers import AutoProcessor, AutoModelForImageTextToText |
| from PIL import Image |
| import requests |
| import torch |
|
|
| model_id = "google/medgemma-4b-it" |
|
|
| model = AutoModelForImageTextToText.from_pretrained( |
| model_id, |
| torch_dtype=torch.bfloat16, |
| device_map="auto", |
| ) |
| processor = AutoProcessor.from_pretrained(model_id) |
|
|
| |
| image_url = "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png" |
| image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw) |
|
|
| messages = [ |
| { |
| "role": "system", |
| "content": [{"type": "text", "text": "You are an expert radiologist."}] |
| }, |
| { |
| "role": "user", |
| "content": [ |
| {"type": "text", "text": "Describe this X-ray"}, |
| {"type": "image", "image": image} |
| ] |
| } |
| ] |
|
|
| inputs = processor.apply_chat_template( |
| messages, add_generation_prompt=True, tokenize=True, |
| return_dict=True, return_tensors="pt" |
| ).to(model.device, dtype=torch.bfloat16) |
|
|
| input_len = inputs["input_ids"].shape[-1] |
|
|
| with torch.inference_mode(): |
| generation = model.generate(**inputs, max_new_tokens=200, do_sample=False) |
| generation = generation[0][input_len:] |
|
|
| decoded = processor.decode(generation, skip_special_tokens=True) |
| print(decoded) |
|
|
