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---
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license: llama2
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base_model:
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- meta-llama/Llama-2-7b
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pipeline_tag: text-generation
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library_name: transformers
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---
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The code below shows how this Buyer Persona generator can be used. |
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This model was developed for [MarketFit.ai](https://danlou.co/marketfitai). |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from tqdm import tqdm |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model_id = "danlou/persona-generator-llama-2-7b-qlora-merged" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float16) |
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def chunks(lst, n): |
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"""Yield successive n-sized chunks from lst.""" |
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for i in range(0, len(lst), n): |
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yield lst[i:i + n] |
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def parse_outputs(output_text): |
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try: |
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output_lns = output_text.split('\n') |
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assert len(output_lns) == 2 |
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assert len(output_lns[0].split(',')) == 2 |
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assert len(output_lns[1]) > 16 |
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name, age = [s.strip() for s in output_lns[0].split(',')] |
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desc = output_lns[1].strip() |
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except AssertionError: |
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raise Exception('Malformed output.') |
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try: |
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age = int(age) |
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except ValueError: |
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raise Exception('Malformed output (age).') |
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return {'name': name, 'age': age, 'description': desc} |
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def generate_personas(product, n=1, batch_size=32, parse=True): |
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prompt = f"### Instruction:\nDescribe the ideal persona for this product:\n{product}\n\n### Response:\n" |
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device) |
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personas = [] |
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with tqdm(total=n) as pbar: |
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for batch in chunks(range(n), batch_size): |
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outputs = model.generate(input_ids, |
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do_sample=True, |
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num_beams=1, |
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num_return_sequences=len(batch), |
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max_length=512, |
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min_length=32, |
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temperature=0.9) |
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for output_ids in outputs: |
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output_decoded = tokenizer.decode(output_ids, skip_special_tokens=True) |
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output_decoded = output_decoded[len(prompt):].strip() |
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try: |
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if parse: |
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personas.append(parse_outputs(output_decoded)) |
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else: |
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personas.append(output_decoded) |
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except Exception as e: |
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print(e) |
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continue |
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pbar.update(len(batch)) |
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return personas |
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product = "Koonie 10000mAh Rechargeable Desk Fan, 8-Inch Battery Operated Clip on Fan, USB Fan, 4 Speeds, Strong Airflow, Sturdy Clamp for Golf Cart Office Desk Outdoor Travel Camping Tent Gym Treadmill, Black (USB Gadgets > USB Fans)" |
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personas = generate_personas(product, n=3) |
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for e in personas: |
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print(e) |
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# Persona 1 - The yoga instructor |
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# {'name': 'Sarah', 'age': 28, 'description': 'Yoga instructor who is passionate about health and fitness. She works from a home studio where she also practices yoga and meditation. Sarah values products that are eco-friendly and sustainable. She loves products that are versatile and can be used for different purposes. Sarah is looking for a product that is durable and can withstand frequent use. She values products that are stylish and aesthetically pleasing.'} |
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# Persona 2 - The golf enthusiast |
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#{'name': 'Sophia', 'age': 60, 'description': "Golf enthusiast. Sophia spends most of her weekends on the golf course, and she needs a fan that she can carry around in her golf cart. She needs a fan that's lightweight, easy to clip on, and has a long battery life. She also wants a fan that's affordable, especially since she plays at different courses."} |
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# Persona 3 - The truck driver |
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# {'name': 'Mike', 'age': 32, 'description': "Truck driver who spends most of his day on the road. The cab of his truck can get hot and stuffy, and Mike needs a fan that can keep him comfortable and alert while he's driving. He needs a fan that's easy to install and adjust, so he can keep it on his dashboard and direct the airflow where he needs it most."} |
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``` |
