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def generate_prompt(keyword): # Define the roles and markers B_SYS, E_SYS = "<>", "<>" B_INST, E_INST = "[INST]", "[/INST]"

catlist=['Baby Products', 'Bags, Wallets and Luggage', 'Beauty', 'Books', 'Car & Motorbike', 'Clothing & Accessories', 'Computers & Accessories', 'Electronics', 'Garden & Outdoors', 'Gift Cards', 'Grocery & Gourmet Foods', 'Health & Personal Care', 'Home & Kitchen', 'Home Improvement', 'Industrial & Scientific', 'Jewellery', 'Kindle Store', 'Movies & TV Shows', 'Music', 'Musical Instruments', 'Office Products', 'Pet Supplies', 'Shoes & Handbags', 'Software', 'Sports, Fitness & Outdoors', 'Toys & Games', 'Video Games', 'Watches']
#catlist=['hair shampoo' , 'other category' ]


# Format your prompt template
prompt = f"""{B_INST} {B_SYS}\nYou are a helpful assistant that provides accurate and concise responses. Do not hallucinate.\n{E_SYS}
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Analyze the following keyword searched on amazon with intent of shopping. Identify the product category from the list {catlist}. Extract the brand from keyword related to brand loyalty intent. Output in JSON with keyword, product category, brand as keys.
### Input:
{keyword.strip()}
{E_INST}\n\n"""



return(prompt)

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Training procedure

The following bitsandbytes quantization config was used during training:

  • quant_method: bitsandbytes
  • load_in_8bit: False
  • load_in_4bit: True
  • llm_int8_threshold: 6.0
  • llm_int8_skip_modules: None
  • llm_int8_enable_fp32_cpu_offload: False
  • llm_int8_has_fp16_weight: False
  • bnb_4bit_quant_type: nf4
  • bnb_4bit_use_double_quant: True
  • bnb_4bit_compute_dtype: bfloat16

Framework versions

  • PEFT 0.6.0.dev0
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