| from transformers import AutoModelForVision2Seq, AutoProcessor | |
| from peft import PeftModel | |
| from PIL import Image | |
| import torch | |
| #LOAD | |
| model = AutoModelForVision2Seq.from_pretrained( | |
| "unsloth/llava-1.5-7b-hf-bnb-4bit", | |
| device_map="auto", | |
| torch_dtype=torch.float16, | |
| ) | |
| model = PeftModel.from_pretrained(model, "grohitraj/archive_classification") | |
| model = model.half() # fix dtype mismatch | |
| processor = AutoProcessor.from_pretrained("grohitraj/archive_classification") | |
| # TEST | |
| image = Image.open("example_from_2019ISIC_data.jpg") | |
| prompt = "<image>\nDescribe about the image for male aged 54:" | |
| inputs = processor(text=prompt, images=image, return_tensors="pt") | |
| inputs = {k: v.to(model.device) for k, v in inputs.items()} | |
| #OUTPUT | |
| with torch.no_grad(): | |
| outputs = model.generate(**inputs, max_new_tokens=100) | |
| print(processor.tokenizer.decode(outputs[0], skip_special_tokens=True)) | |