from transformers import AutoProcessor, AutoModelForCausalLM
from peft import PeftModel
import torch

# Base model
base_model_id = "google/paligemma2-3b-mix-224"
adapter_id = "yu3733/paligemma2-3b-lora-vqa-d1000-r8"

# Load processor
processor = AutoProcessor.from_pretrained(base_model_id)

# Load base model
model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(model, adapter_id)

# Inference
prompt = "<image>\nQuestion: What is in this image?\nAnswer:"
inputs = processor(text=prompt, images=image, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(processor.decode(outputs[0], skip_special_tokens=True))

Training Details

Base Model: google/paligemma2-3b-mix-224
Training Data: VizWiz VQA Dataset
LoRA Rank: 8
Training Framework: PEFT + Transformers

License

Same as the base model (see google/paligemma2-3b-mix-224)