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from peft import PeftModel |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load base model |
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base_model = AutoModelForCausalLM.from_pretrained( |
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"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", |
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load_in_4bit=True, |
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device_map="auto" |
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) |
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# Load NEXTa adapters |
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model = PeftModel.from_pretrained(base_model, "NEXTa-SA/Nexta-39-23") |
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tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B") |
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# Example prompt structure |
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prompt = """ |
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Task: Create social media post |
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Language: English |
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Brand: [Brand name] |
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Audience: [Target audience] |
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Objective: [Campaign objective] |
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Tone: [Desired tone] |
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Additional context: [Any specific requirements] |
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Generate a social media post that: |
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""" |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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outputs = model.generate( |
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**inputs, |
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max_new_tokens=300, |
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temperature=0.7, |
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top_p=0.9, |
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do_sample=True |
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) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(response) |