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  1. app.py +44 -0
  2. requirements.txt +7 -0
app.py ADDED
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+ import torch
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+ from peft import PeftModel, PeftConfig
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ peft_model_id = f"FourthBrainGenAI/MarketMail-AI-Model"
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+ config = PeftConfig.from_pretrained(peft_model_id)
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+ model = AutoModelForCausalLM.from_pretrained(
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+ config.base_model_name_or_path,
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+ return_dict=True,
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+ load_in_8bit=True,
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+ device_map="auto",
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+ )
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+ tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
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+
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+ # Load the Lora model
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+ model = PeftModel.from_pretrained(model, peft_model_id)
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+
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+
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+ def make_inference(product, description):
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+ batch = tokenizer(
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+ f"Below is a product and description, please write a marketing email for this product.\n\n### Product:\n{product}\n### Description:\n{description}\n\n### Marketing Email",
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+ return_tensors="pt",
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+ )
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+
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+ with torch.cuda.amp.autocast():
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+ output_tokens = model.generate(**batch, max_new_tokens=50)
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+
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+ return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
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+
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+
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+ if __name__ == "__main__":
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+ # make a gradio interface
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+ import gradio as gr
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+
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+ gr.Interface(
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+ make_inference,
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+ [
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+ gr.inputs.Textbox(lines=2, label="Product Name"),
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+ gr.inputs.Textbox(lines=5, label="Product Description"),
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+ ],
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+ gr.outputs.Textbox(label="Ad"),
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+ title="MarketMail-AI",
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+ description="MarketMail-AI is a tool that generates marketing emails for products.",
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+ ).launch()
requirements.txt ADDED
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+ bitsandbytes
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+ datasets
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+ accelerate
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+ loralib
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+ gradio
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+ git+https://github.com/huggingface/peft.git
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+ git+https://github.com/huggingface/transformers.git@main