import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel, PeftConfig

# 1. Load the Base Model (GPT-2) with 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

base_model_id = "openai-community/gpt2-medium"
model = AutoModelForCausalLM.from_pretrained(
    base_model_id, 
    quantization_config=bnb_config, 
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(base_model_id)

# 2. Load the LoRA Adapters
peft_model_id = "estradax/gpt2-medium-alpaca-4bit"
model = PeftModel.from_pretrained(model, peft_model_id)

# 3. Run Inference
text = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nWhat is the capital of France?\n\n### Response:\n"
inputs = tokenizer(text, return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))