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import torch
from transformers import LlavaForConditionalGeneration, BitsAndBytesConfig, AutoProcessor
from peft import PeftModel
import requests
from PIL import Image
def load_base_model(model_id):
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
base_model = LlavaForConditionalGeneration.from_pretrained(model_id,
quantization_config=quantization_config,
torch_dtype=torch.float16)
return base_model
def load_peft_lora_adapter(base_model, peft_lora_adapter_path):
peft_lora_adapter = PeftModel.from_pretrained(base_model, peft_lora_adapter_path, adapter_name="lora_adapter")
return peft_lora_adapter
def merge_adapters(base_model, peft_lora_adapter_path):
base_model.load_adapter(peft_lora_adapter_path, adapter_name="lora_adapter")
return base_model
def main():
model_id = "llava-hf/llava-1.5-7b-hf" # Actual base model id
peft_lora_adapter_path = 'somnathsingh31/llava-1.5-7b-hf-ft-museum' # Actual adapter path
# Load the base model
base_model = load_base_model(model_id)
# Load the PEFT Lora model (adapter)
peft_lora_adapter = load_peft_lora_adapter(base_model, peft_lora_adapter_path)
# Merge the adapters into the base model
merged_model = merge_adapters(base_model, peft_lora_adapter_path)
prompt = "USER: <image>\nWhat is special in this chess set and pieces? \nASSISTANT:"
url = "https://images.metmuseum.org/CRDImages/ad/original/138425.jpg"
image = Image.open(requests.get(url, stream=True).raw)
processor = AutoProcessor.from_pretrained(model_id)
inputs = processor(text=prompt, images=image, return_tensors="pt")
# ... process the image and create inputs ...
generate_ids = merged_model.generate(**inputs, max_new_tokens=150)
decoded_response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print("Generated response:", decoded_response)
if __name__ == "__main__":
main()
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