product_recommendation_ai_powered

print("="*50) print("CREATING PROFESSIONAL MODEL CARD") print("="*50)

Create a detailed model card

model_card = """

language: en license: apache-2.0 library_name: transformers pipeline_tag: text-generation tags:

• product-recommendations
• e-commerce
• customer-reviews
• fine-tuned
• qwen2
• text-generation

---

🚀 Product Recommendation AI

A fine-tuned Qwen2 1.5B model that generates professional product recommendation guides based on customer reviews and ratings.

🎯 What does this model do?

This AI model creates comprehensive product buying guides by analyzing customer sentiment and review patterns. It's perfect for:

• E-commerce product recommendations
• Customer review analysis
• Product comparison guides
• Buying decision support

💻 Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the model
model = AutoModelForCausalLM.from_pretrained(
    "Ishumalai/product_recommendation_ai_powered",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Ishumalai/product_recommendation_ai_powered")

def generate_recommendation(category):
    messages = [{"role": "user", "content": f"Create a product recommendation guide for {category}"}]
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=500,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    if "<|im_start|>assistant" in response:
        return response.split("<|im_start|>assistant")[-1].replace("<|im_end|>", "").strip()
    return response

# Generate a guide
guide = generate_recommendation("Wireless Headphones")
print(guide)