Llm Fine-Tuning Mastery: Basic To Advanced & Cloud Deploy

Professional LLM Fine-Tuning: LoRA, QLoRA, RLHF -DPO Techniques,Hugging face + Azure, AWS, GCP Cloud Deployment in 2025

What you'll learn

Use LoRA and QLoRA adapters to fine-tune BERT, GPT, Llama, Mistral and DeepSeek with minimal GPU memory.

Run RLHF Direct Preference Optimization workflows to align model outputs with human feedback.

Perform supervised instruction tuning to build domain datasets and update weights for task-specific accuracy gains.

Compress large teachers into efficient students via knowledge distillation, transferring soft targets and hidden-feature signals.

Requirements

Python

Basic of Generative AI

Description

Master the complete spectrum of Large Language Model fine-tuning with the most comprehensive hands-on course available today. This intensive program transforms you from foundational concepts to enterprise-level deployment, covering cutting-edge techniques across multiple architectures and cloud platforms.What You'll LearnAdvanced Fine-Tuning Methodologies:Master LoRA (Low-Rank Adaptation) for parameter-efficient training that reduces computational costs while maintaining model performance23Implement QLoRA (Quantized LoRA) for memory-optimized fine-tuning in resource-constrained environmentsDeploy RLHF (Reinforcement Learning ) to create aligned AI systems that follow human preferencesApply DPO (Direct Preference Optimization) for improved model behavior without complex reinforcement learning pipelinesApply Model Distillation for Knowledge transfer from a large model to a smaller modelMulti-Architecture Model Training:Fine-tune BERT models for specialized text understanding and classification tasksCustomize Mistral models for domain-specific applications requiring efficient performanceAdapt GPT architectures for conversational AI  text generation systemsOptimize LLaMA models for professional-grade applicationsConfigure Cohere models for production-ready natural language processing workflowsDeploy on Hugging Face Hub: Master model uploading, versioning, and sharing using push_to_hub() functionality for seamless model distributionEnterprise Cloud Platform Mastery:Azure AI Foundry: Build, deploy, and manage enterprise-grade AI applications with integrated development environmentsAWS Bedrock: Implement scalable fine-tuning workflows using S3, Lambda, and API Gateway for AI-powered applicationsGCP Vertex AI: Leverage parameter-efficient tuning and full fine-tuning approaches with supervised learning methodologiesKey Learning OutcomesTransform your AI expertise through hands-on projects that simulate real-world enterprise scenarios. Experience comprehensive dataset preparation, from raw data to production-ready training formats. Master performance optimization techniques including hyperparameter tuning, model evaluation metrics, and cost management strategies across cloud platforms. Build end-to-end deployment pipelines that scale from prototype to enterprise production environments.Course JourneyBegin with transformer architecture fundamentals before progressing through parameter-efficient training methodologies. Each technique is reinforced through practical coding sessions using industry-standard datasets and real-world use cases. Experience comprehensive cloud platform integration across Azure, AWS, and GCP ecosystems, learning platform-specific optimization strategies and cross-platform migration techniques.Who Should EnrollDesigned for intermediate to advanced AI practitioners, including machine learning engineers, data scientists, AI researchers, and software developers seeking specialization in LLM customization. Basic Python programming knowledge and familiarity with machine learning concepts are recommended.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 All Fine Tuning Code and Data Resource

Section 2: Bert Model Fine Tune

Lecture 3 Bert Fine tune overview

Lecture 4 Neural network fine tune Intro

Lecture 5 Bert Fine Tune code library code

Lecture 6 NER dataset overview

Lecture 7 Bert NER data chunking code

Lecture 8 Tokenization Thoery

Lecture 9 Tokenization data code

Lecture 10 Tokenization and ALign label fun code

Lecture 11 Sample Accuracy testing

Lecture 12 Training Bert Model

Lecture 13 Evalualte Fine Tuned Model

Lecture 14 Publish your model to Hugging face

Section 3: Mistral Model -LORA QLORA Fine Tune

Lecture 15 Lora Fine Tune Intro

Lecture 16 QLORA Fine Tune Intro

Lecture 17 Hugging face API create

Lecture 18 Library and dataset Intro Samsun Mistrael

Lecture 19 Load Dataset Samsun

Lecture 20 QLORA Model code

Lecture 21 LORA model config code

Lecture 22 Train and Publish the model

Lecture 23 Inference the Model

Lecture 24 Testing the Fine Tune model

Section 4: OpenAI GPT Fine Tune

Lecture 25 GPT Fine Tune Overview

Lecture 26 Train the GPT Model

Lecture 27 Inference GPT Model

Lecture 28 Data Preparation Fine Tune

Section 5: RLHFD DPO Fine Tune Llama model

Lecture 29 Reinforcement learning overview

Lecture 30 SFT Overview DPO code

Lecture 31 RLHF DPO Overview code

Lecture 32 SFT training code

Lecture 33 DPO Overview

Lecture 34 Tatsu lab alpaca Dataset code

Lecture 35 SFT Training and Save Model

Lecture 36 DPO config Overview

Lecture 37 DPO Training with preference data

Lecture 38 Training and Save Model Hugging face

Lecture 39 DPO Fine tune model Inferencing

Section 6: Model Distillation

Lecture 40 Model Distillation Intro

Lecture 41 What is LLMs Distillation

Lecture 42 Knowledge Distillation Architecture

Lecture 43 Library install of Distillation

Lecture 44 Data Filter code

Lecture 45 Prompt Code

Lecture 46 Token price in Fine tune

Lecture 47 Chat completion Response format

Lecture 48 Parallel processing

Lecture 49 GPT data processing

Lecture 50 GPT training

Lecture 51 Model distillation Fine Tune

Lecture 52 Check fine tune status automatically

Lecture 53 Fine Tune Dashboard

Lecture 54 Distilled model Testing

Section 7: Deepseek-Supervised Fine Tune-(SFT)

Lecture 55 Deepseek model Intro

Lecture 56 Supervised Fine Tuning

Lecture 57 Dataset intro

Lecture 58 SFT code part1

Lecture 59 SFT Code Part 2

Lecture 60 SFT Load model dataset

Lecture 61 SFT Training

Lecture 62 SFT Training the dataset

Lecture 63 SFT Model dataset

Lecture 64 SFT Infernce

Lecture 65 Custom model Inferencing and Save

Section 8: Azure Fine Tune

Lecture 66 Open Azure Free Account

Lecture 67 Azure subscription create (Optional)

Lecture 68 Azure AI Foundry Resource

Lecture 69 Azure cost Management

Lecture 70 Azure Fine Tune Intro

Lecture 71 AI Foundry Overview

Lecture 72 Tourism Dataset Overview

Lecture 73 Azure BLOB upload dataset

Lecture 74 Supervised Fine Tuning

Lecture 75 SFT Completion Overview

Lecture 76 Deployment Fine Tuned Model

Lecture 77 Demo Azure Fine Tuned Tourism Model

Lecture 78 Delete unused Resources

Section 9: AWS Fine Tune

Lecture 79 AWS create Account

Lecture 80 Dataset Overview

Lecture 81 Load Dataset S3

Lecture 82 Model Access

Lecture 83 AWS Bedrock-Fine Tune model

Lecture 84 Validation status

Lecture 85 Fine Tune Validation

Lecture 86 Delete Unused Resources

Section 10: Google Cloud Fine Tune

Lecture 87 Create GCP Account Free

Lecture 88 Vertex AI Overview

Lecture 89 Dataset Overview

Lecture 90 Fine Tuning Vertex AI

Lecture 91 Training Details

Lecture 92 Fine Tuned Model Testing

Lecture 93 Delete unused Resources

Section 11: Bonus Python Tutorial

Lecture 94 Python Architecture

Lecture 95 Print and Comment command

Lecture 96 Variables

Lecture 97 Data Type Number

Lecture 98 Data Type String

Lecture 99 Data Type Boolean

Lecture 100 Operator Python

Lecture 101 Collection List

Lecture 102 Collection Set

Lecture 103 Collection Dictionary

Lecture 104 If else Condition

Lecture 105 While Loop

Lecture 106 For Loop

Lecture 107 Function Intro

Lecture 108 Function Code

Lecture 109 Collection Tuple

Lecture 110 Lamda Python

Lecture 111 Array Function

Lecture 112 Class blueprint

Lecture 113 Python init

Lecture 114 Class str

Lecture 115 Class function

Lecture 116 Inheritance Intro

Lecture 117 Inheritance with init

Lecture 118 Iterator python

Lecture 119 Polymorphism

Lecture 120 Scope

Lecture 121 Modules

Lecture 122 Dates

Lecture 123 Math Modules

Lecture 124 Regular expression

Lecture 125 JSON Object

Lecture 126 PIP Package

Lecture 127 Exception handling

Lecture 128 User Input

Lecture 129 String format

Lecture 130 File Read

Lecture 131 File Write

Fine tune LLMs