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  Use the trained smangrul/peft_lora_e5_ecommerce_semantic_search_colab model to get the product embeddings:
  
  
  	Copied
  # base model
  model = AutoModelForSentenceEmbedding(model_name_or_path, tokenizer)
  
  # peft config and wrapping
  model = PeftModel.from_pretrained(model, peft_model_id)
  
  device = "cuda"
  model.to(device)
  model.eval()
  model = model.merge_and_unload()
  
  import numpy as np
  num_products= len(dataset)
  d = 1024
  
  product_embe