from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
    load_in_4bit=True,
    device_map="auto"
)

# Load NEXTa adapters
model = PeftModel.from_pretrained(base_model, "NEXTa-SA/Nexta-39-23")
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")

# Example prompt structure
prompt = """
Task: Create social media post
Language: English
Brand: [Brand name]
Audience: [Target audience]
Objective: [Campaign objective]
Tone: [Desired tone]
Additional context: [Any specific requirements]

Generate a social media post that:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=300,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)