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| import torch | |
| from peft import PeftModel, PeftConfig | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| peft_model_id = f"FourthBrainGenAI/GenerAd-AI" | |
| config = PeftConfig.from_pretrained(peft_model_id) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| config.base_model_name_or_path, | |
| return_dict=True, | |
| load_in_8bit=True, | |
| device_map="auto", | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) | |
| # Load the Lora model | |
| model = PeftModel.from_pretrained(model, peft_model_id) | |
| def make_inference(product_name, product_description): | |
| batch = tokenizer( | |
| f"### Product and Description:\n{product_name}: {product_description}\n\n### Ad:", | |
| return_tensors="pt", | |
| ) | |
| with torch.cuda.amp.autocast(): | |
| output_tokens = model.generate(**batch, max_new_tokens=50) | |
| return tokenizer.decode(output_tokens[0], skip_special_tokens=True) | |
| if __name__ == "__main__": | |
| # make a gradio interface | |
| import gradio as gr | |
| gr.Interface( | |
| make_inference, | |
| [ | |
| gr.inputs.Textbox(lines=2, label="Product Name"), | |
| gr.inputs.Textbox(lines=5, label="Product Description"), | |
| ], | |
| gr.outputs.Textbox(label="Ad"), | |
| title="GenerAd-AI", | |
| description="GenerAd-AI is a generative model that generates ads for products.", | |
| ).launch() | |