| 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) |