praneethposina/customer_support_bot

Customer Support Chatbot with LLaMA 3.1

An end-to-end customer support chatbot solution powered by fine-tuned LLaMA 3.1 8B model, deployed using Flask, Docker, and AWS ECS.

Overview

This project implements a sophisticated customer support chatbot leveraging the LLaMA 3.1 8B model fine-tuned on customer support conversations. The solution uses LoRA fine-tuning and various quantization techniques for optimized inference, deployed as a containerized application on AWS ECS with Fargate.

Features

• Fine-tuned LLaMA 3.1 Model: Customized for customer support using the Bitext customer support dataset
• Optimized Inference: Implements 4-bit, 8-bit, and 16-bit quantization
• Containerized Deployment: Docker-based deployment for consistency and scalability
• Cloud Infrastructure: Hosted on AWS ECS with Fargate for serverless container management
• CI/CD Pipeline: Automated deployment using AWS CodePipeline
• Monitoring: Comprehensive logging and monitoring via AWS CloudWatch

Model Details

The fine-tuned model is hosted on Hugging Face:

• Model Repository: praneethposina/customer_support_bot
• Github Repository: github.com/praneethposina/Customer_Support_Chatbot
• Base Model: LLaMA 3.1 8B
• Training Dataset: Bitext Customer Support Dataset
• Optimization: LoRA fine-tuning with quantization

Tech Stack

• Backend: Flask API
• Model Serving: Ollama
• Containerization: Docker
• Cloud Services:
  • AWS ECS (Fargate)
  • AWS CodePipeline
  • AWS CloudWatch
• Model Training: LoRA, Quantization

Screenshots

Chatbot Interface

AWS CloudWatch Monitoring

Docker Logs

AWS Deployment

1. Push Docker image to Amazon ECR
2. Configure AWS ECS Task Definition
3. Set up AWS CodePipeline for CI/CD
4. Configure CloudWatch monitoring

Uploaded model

• Developed by: praneethposina
• License: apache-2.0
• Finetuned from model : unsloth/llama-3-8b-bnb-4bit