Update README.md

README.md

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----
-base_model: unsloth/llama-3-8b-bnb-4bit
-language:
-- en
-license: apache-2.0
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- llama
-- gguf
-datasets:
-- bitext/Bitext-customer-support-llm-chatbot-training-dataset
-pipeline_tag: text-generation
----
-
-# Uploaded  model
-
-- **Developed by:** praneethposina
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
+# 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](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-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](https://huggingface.co/praneethposina/customer_support_bot)
+- Github Repository: (https://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
+
+![Chatbot SS](https://github.com/user-attachments/assets/220aea77-bb2b-4f50-b6a4-0541434d85ef)
+
+![Chatbot SS2](https://github.com/user-attachments/assets/da440735-59d7-4be7-a43d-d51de8983738)
+
+### AWS CloudWatch Monitoring
+
+![CloudWatch SS](https://github.com/user-attachments/assets/9794bc3e-4b9c-4626-9a7f-3936d4757328)
+
+### Docker Logs
+
+<img width="1270" alt="Docker ss" src="https://github.com/user-attachments/assets/a72d1c35-8203-4a05-b944-743ea6c0a6b8" />
+<img width="1268" alt="Docker ss2" src="https://github.com/user-attachments/assets/f1b0c0b1-2aad-462c-adf2-7a7ea9047a1a" />
+
+## 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
+
+