finetuned_devops_helper

🛠️ DevOps Helper with Fine-tuned LLM

A fine-tuned Large Language Model for DevOps question answering, built with JFrogML platform integration.

🚀 Quick Start

Choose your preferred workflow:

Option 1: Local Model Experimentation & Registry

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   💻 Local      │ -> │   📦 Model      │ -> │   🏗️ Build &    │
│   Training      │    │   Logging to    │    │   Deploy in     │
│   (Notebook)    │    │   JFrog ML      │    │   JFrogML UI    │
│                 │    │   Registry      │    │                 │
└─────────────────┘    └─────────────────┘    └─────────────────┘

🎯 Recommended for: Local experimentation and model version logging

Complete workflow: 📓 DevOps Helper Training Notebook Guide

Option 2: ML App Code → Build → Deploy

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   💻 ML App     │ -> │   🏗️ Build      │ -> │   🚀 Deploy     │
│   Code          │    │   (w/ Training  │    │   Real-time API │
│   (main/)       │    │   Job)          │    │   Endpoints     │
└─────────────────┘    └─────────────────┘    └─────────────────┘

🎯 Recommended for: Standardized, production-ready workflows

Complete workflow: 🚀 Remote Training & Deployment Guide

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📋 Prerequisites

• Python: 3.9-3.11
• JFrog Account: Sign up free
• Hugging Face Account: For LLM access

📁 Project Structure

finetuned_devops_helper/
├── main/                           # Core JFrogML model code
│   ├── __init__.py                # Python package marker
│   ├── conda.yaml                 # Environment dependencies
│   ├── model.py                   # LLMFineTuner class with build() and predict()
│   ├── config.py                  # Model configuration and hyperparameters
│   ├── data_utils.py              # Dataset loading and preprocessing utilities
│   └── model_utils.py             # Model loading and hardware optimization utilities
├── test_model_code_locally.py     # Local model testing script
├── test_live_endpoint.py          # Real-time endpoint testing script
└── README.md                      # This file

🎯 Model Overview

Technology: Fine-tuned Llama2 8B model using LoRA (Low-Rank Adaptation) Domain: DevOps question answering and assistance Method: Parameter-efficient fine-tuning for domain-specific responses

📚 Related Resources

• JFrogML Documentation
• Model Deployment Strategies
• JFrog Artifactory ML Repositories

🚀 Next Steps

1. Choose your workflow above based on your use case
2. Follow the linked guides for step-by-step instructions
3. Deploy and serve your fine-tuned DevOps assistant