Alkyema/FAQ_ChatBot

Model Card for Model ID

FAQ Chatbot for Online Orders and Website Queries

This model is a large language model (LLM) based on the LLaMA 3 architecture, fine-tuned to handle frequently asked questions (FAQ) related to online orders and website queries. It is designed to provide accurate and helpful responses to common customer inquiries.

Model Details

• Model Name: FAQ Chatbot for Online Orders and Website Queries
• Architecture: LLaMA 3
• Training Data: This model was trained on a dataset consisting of typical customer queries related to online orders, such as order status, payment issues, returns and refunds, shipping information, and general website navigation.
• Usage: The model is intended to be used as a customer support assistant, capable of addressing a wide range of questions about online shopping and website functionality.

Features

• Natural Language Understanding: The model can understand and process natural language input, making it user-friendly for customers.
• Contextual Responses: Provides responses that are contextually relevant to the user's query.
• Scalable Support: Can handle a high volume of queries simultaneously, improving customer service efficiency.

Example Queries

Here are some example queries that the model can handle:

1. Order Status: "Can you tell me the status of my order #12345?"
2. Payment Issues: "I'm having trouble processing my payment. Can you help?"
3. Returns and Refunds: "How can I return a product I bought?"
4. Shipping Information: "When will my order be delivered?"
5. Website Navigation: "How do I find the size chart on your website?"

How to Use

To use this model, you can integrate it into your customer support system or chatbot framework. Here's a basic example using the Hugging Face transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "your-hugging-face-username/faq-chatbot-online-orders"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example query
query = "Can you tell me the status of my order #12345?"

# Tokenize the input
inputs = tokenizer(query, return_tensors="pt")

# Generate response
outputs = model.generate(**inputs)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
```python


This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Satwik Kishore
- **Model type:** Text Generation
- **Language(s) (NLP):** English