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from transformers import AutoModelForCausalLM, AutoTokenizer
food = ["Cheese dip with chili pepper",
"Pepperoni, NFS",
"Pepperoni, reduced fat",
"Pepperoni, reduced sodium",
"Stuffed green pepper, Puerto Rican style",
"Pepper steak",
"Sausage and peppers, no sauce",
"Pepperpot soup",
"Pizza with pepperoni, from frozen, thin crust",
"Pizza with pepperoni, from frozen, medium crust",
"Pizza with pepperoni, from frozen, thick crust",
"Pizza with pepperoni, from restaurant or fast food, NS as to type of crust",
"Pizza with pepperoni, from restaurant or fast food, thin crust",
"Pizza with pepperoni, from restaurant or fast food, medium crust",
"Pizza with pepperoni, from restaurant or fast food, thick crust",
"Pizza with pepperoni, stuffed crust",
"Pizza with pepperoni, from school lunch, thin crust",
"Pizza, with pepperoni, from school lunch, medium crust",
"Pizza with pepperoni, from school lunch, thick crust",
"Pizza with meat other than pepperoni, from frozen, thin crust",
"Pizza with meat other than pepperoni, from frozen, medium crust",
"Pizza with meat other than pepperoni, from frozen, thick crust",
"Pizza with meat other than pepperoni, from restaurant or fast food, NS as to type of crust",
"Pizza with meat other than pepperoni, from restaurant or fast food, thin crust",
"Pizza with meat other than pepperoni, from restaurant or fast food, medium crust",
"Pizza with meat other than pepperoni, from restaurant or fast food, thick crust",
"Pizza, with meat other than pepperoni, stuffed crust",
"Pizza, with meat other than pepperoni, from school lunch, medium crust",
"Pizza, with meat other than pepperoni, from school lunch, thin crust",
"Pizza, with meat other than pepperoni, from school lunch, thick crust",
"Stuffed pepper, with meat",
"Stuffed pepper, with rice and meat",
"Stuffed pepper, with rice, meatless",
"Pepper, hot chili, raw",
"Pepper, raw, NFS",
"Pepper, sweet, green, raw",
"Pepper, sweet, red, raw",
"Pepper, banana, raw",
"Seven-layer salad, lettuce salad made with a combination of onion, celery, green pepper, peas, mayonnaise, cheese, eggs, and/or bacon",
"Peppers, green, cooked",
"Peppers, red, cooked",
"Hot peppers, cooked",
"Peppers and onions, cooked, no added fat",
"Peppers and onions, cooked, fat added",
"Stuffed jalapeno pepper",
"Hot pepper sauce",
"Peppers, pickled",
"Pepper, hot, pickled",
"Pepper, for use on a sandwich",
"Soft drink, pepper type",
"Soft drink, pepper type, diet",
"Soft drink, pepper type, decaffeinated",
"Soft drink, pepper type, decaffeinated, diet",
"Green pepper, cooked, as ingredient",
"Red pepper, cooked, as ingredient",
"Pepperidge Farm, Goldfish, Baked Snack Crackers, Original",
"Pepperidge Farm, Goldfish, Baked Snack Crackers, Parmesan",
"Pepperidge Farm, Goldfish, Baked Snack Crackers, Pizza",
"Candies, YORK Peppermint Pattie",
"Pepperidge Farm, Goldfish, Baked Snack Crackers, Cheddar",
"Pepperidge Farm, Goldfish, Baked Snack Crackers, Explosive Pizza",
"Salad dressing, peppercorn dressing, commercial, regular",
"HORMEL ALWAYS TENDER, Pork Tenderloin, Peppercorn-Flavored",
"Peppers, sweet, red, canned, solids and liquids",
"Peppers, sweet, red, frozen, chopped, unprepared",
"Peppers, sweet, red, frozen, chopped, boiled, drained, without salt",
"Peppers, sweet, red, frozen, chopped, boiled, drained, with salt",
"Peppers, sweet, red, sauteed",
"Peppers, hot chile, sun-dried",
"Peppers, jalapeno, raw",
"Peppers, chili, green, canned",
"Peppers, hungarian, raw",
"Peppers, pasilla, dried",
"Corn with red and green peppers, canned, solids and liquids",
"Peppers, sweet, red, freeze-dried",
"Peppers, sweet, yellow, raw",
"Peppers, serrano, raw",
"Peppers, ancho, dried",
"Peppers, hot pickled, canned",
"Peppers, sweet, green, frozen, chopped, unprepared",
"Peppers, sweet, green, frozen, chopped, boiled, drained, without salt",
"Peppers, sweet, green, sauteed",
"Peppers, jalapeno, canned, solids and liquids",
"Peppers, hot chili, red, raw",
"Peppers, hot chili, red, canned, excluding seeds, solids and liquids",
"Peppers, sweet, red, raw",
"Peppers, sweet, green, cooked, boiled, drained, with salt",
"Peppers, sweet, red, cooked, boiled, drained, without salt",
"PIZZA HUT 14' Pepperoni Pizza, THIN 'N CRISPY Crust",
"DOMINO'S 14' Pepperoni Pizza, Crunchy Thin Crust",
"Peppers, hot chili, green, canned, pods, excluding seeds, solids and liquids",
"Peppers, sweet, green, raw",
"Peppers, sweet, green, cooked, boiled, drained, without salt",
"Peppers, sweet, green, canned, solids and liquids",
"Tomato products, canned, sauce, with onions, green peppers, and celery",
"Peppers, hot chili, green, raw",
"Peppers, sweet, red, cooked, boiled, drained, with salt",
"Peppers, sweet, green, frozen, chopped, cooked, boiled, drained, with salt",
"School Lunch, pizza, BIG DADDY'S LS 16' 51% Whole Grain Rolled Edge Turkey Pepperoni Pizza, frozen",
"School Lunch, pizza, TONY'S SMARTPIZZA Whole Grain 4x6 Pepperoni Pizza 50/50 Cheese, frozen",
"DIGIORNO Pizza, pepperoni topping, cheese stuffed crust, frozen, baked",
"DIGIORNO Pizza, pepperoni topping, rising crust, frozen, baked",
"DIGIORNO Pizza, pepperoni topping, thin crispy crust, frozen, baked",
"Fast Food, Pizza Chain, 14' pizza, pepperoni topping, thin crust",
"School Lunch, pizza, pepperoni topping, thin crust, whole grain, frozen, cooked",
"School Lunch, pizza, pepperoni topping, thick crust, whole grain, frozen, cooked",
"Spices, pepper, black",
"Spices, pepper, red or cayenne",
"Spices, pepper, white",
"Sauce, peppers, hot, chili, mature red, canned",
"Sauce, chili, peppers, hot, immature green, canned",
"Beverages, carbonated, low calorie, cola or pepper-types, with sodium saccharin, contains caffeine",
"DOMINO'S 14' Pepperoni Pizza, Classic Hand-Tossed Crust",
"DOMINO'S 14' Pepperoni Pizza, Ultimate Deep Dish Crust",
"LITTLE CAESARS 14' Original Round Pepperoni Pizza, Regular Crust",
"PIZZA HUT 14' Pepperoni Pizza, Hand-Tossed Crust",
"PIZZA HUT 14' Pepperoni Pizza, Pan Crust",
"Pizza, pepperoni topping, regular crust, frozen, cooked",
"Beverages, carbonated, low calorie, other than cola or pepper, without caffeine",
"Beverages, carbonated, low calorie, other than cola or pepper, with aspartame, contains caffeine",
"Beverages, carbonated, pepper-type, contains caffeine",
"PIZZA HUT 12' Pepperoni Pizza, Hand-Tossed Crust",
"PIZZA HUT 12' Pepperoni Pizza, Pan Crust",
"PAPA JOHN'S 14' Pepperoni Pizza, Original Crust",
"LITTLE CAESARS 14' Pepperoni Pizza, Large Deep Dish Crust",
"Fast Food, Pizza Chain, 14' pizza, pepperoni topping, regular crust",
"Fast Food, Pizza Chain, 14' pizza, pepperoni topping, thick crust",
"Peppermint, fresh",
"Sauce, ready-to-serve, pepper or hot",
"Sauce, ready-to-serve, pepper, TABASCO",
"Peppered loaf, pork, beef",
"Pepperoni, beef and pork, sliced",
"Hormel Pillow Pak Sliced Turkey Pepperoni",
"Turkey, breast, smoked, lemon pepper flavor, 97% fat-free",
"Beverages, carbonated, low calorie, cola or pepper-type, with aspartame, without caffeine",
"Beverages, carbonated, low calorie, cola or pepper-type, with aspartame, contains caffeine",
"Carbonated beverage, low calorie, other than cola or pepper, with sodium saccharin, without caffeine",
"NFY0907NU",
"NFY0907N6",
"NFY0907OU",
"OATMEAL COOKIES WITH RAISINS, PEPPERIDGE FARM SOFT BAKED",
"Peppers, bell, green, raw",
"Peppers, bell, yellow, raw",
"Peppers, bell, red, raw",
"Peppers, bell, orange, raw",
"peppers, bell, green, raw",
"peppers, bell, red, raw",
"peppers, bell, yellow, raw",
"peppers, bell, orange, raw",
"peppers, bell, red, raw, mini ",
"peppers, bell, orange, raw, mini "]
# Load the tokenizer and model from Hugging Face
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-instruct")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-instruct")
# Define the input message
input_text = "Return the name in a short JSON object. What item from this list is most similar to: 'Pepper - Habanero Pepper'. List: " + "; ".join(food)
# Tokenize the input
inputs = tokenizer(input_text, return_tensors="pt")
# Generate the response
outputs = model.generate(inputs["input_ids"], max_new_tokens=50, pad_token_id=tokenizer.eos_token_id)
# Decode the output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
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