Visão Geral

Este curso aborda as técnicas e estratégias de ajuste fino (Fine-Tuning) de Large Language Models (LLMs), capacitando os participantes a adaptar modelos de linguagem para domínios específicos, requisitos corporativos e casos de uso especializados. O curso explora desde os fundamentos do treinamento e adaptação de modelos até técnicas modernas como Instruction Tuning, Supervised Fine-Tuning (SFT), Parameter-Efficient Fine-Tuning (PEFT), LoRA, QLoRA e alinhamento de modelos. O participante aprenderá a planejar, executar, avaliar e operar projetos de customização de LLMs em ambientes corporativos.

Objetivo

Após realizar este curso, você será capaz de:

• Compreender os fundamentos do Fine-Tuning de Large Language Models
• Selecionar estratégias adequadas de customização para diferentes cenários de negócio
• Preparar datasets para treinamento supervisionado e adaptação de modelos
• Implementar técnicas modernas de Fine-Tuning com eficiência computacional
• Avaliar qualidade, desempenho e alinhamento de modelos ajustados
• Operar e governar modelos customizados em ambientes corporativos

Publico Alvo

• Engenheiros de Machine Learning
• Engenheiros de IA Generativa
• Cientistas de Dados
• Desenvolvedores de soluções de IA
• Arquitetos de IA e Machine Learning
• Pesquisadores interessados em customização de modelos de linguagem

Pre-Requisitos

• Conhecimentos de Machine Learning e Deep Learning
• Familiaridade com Python e bibliotecas de IA
• Conhecimentos básicos de Large Language Models e Transformers
• Experiência com ambientes Linux e GPUs é recomendada

Conteúdo Programatico

Module 1: Introduction to LLM Fine-Tuning

1. Fundamentals of model adaptation
2. Fine-tuning versus prompting
3. Enterprise use cases for customization
4. Benefits and limitations of fine-tuning
5. LLM customization strategies
6. Fine-tuning lifecycle overview

Module 2: Foundations of Large Language Models

1. Transformer architecture review
2. Pre-training concepts
3. Language model behavior
4. Embeddings and representations
5. Inference and generation fundamentals
6. Model capabilities and constraints

Module 3: Dataset Preparation for Fine-Tuning

1. Data collection strategies
2. Dataset design principles
3. Data cleaning and normalization
4. Instruction dataset creation
5. Labeling and annotation approaches
6. Data quality assessment

Module 4: Supervised Fine-Tuning (SFT)

1. SFT fundamentals
2. Training objectives
3. Supervised learning workflows
4. Instruction tuning concepts
5. Training dataset structures
6. SFT best practices

Module 5: Parameter-Efficient Fine-Tuning (PEFT)

1. Introduction to PEFT
2. Adapter-based architectures
3. LoRA fundamentals
4. QLoRA concepts
5. Efficiency and scalability considerations
6. Model adaptation trade-offs

Module 6: Advanced Fine-Tuning Techniques

1. Domain adaptation methodologies
2. Task-specific optimization
3. Multi-task fine-tuning
4. Continual learning concepts
5. Transfer learning approaches
6. Advanced tuning strategies

Module 7: Alignment and Model Behavior

1. Model alignment fundamentals
2. Human feedback concepts
3. Preference optimization approaches
4. Safety alignment strategies
5. Reducing harmful outputs
6. Behavioral control techniques

Module 8: Evaluation and Benchmarking

1. Evaluation methodologies
2. Accuracy and quality metrics
3. Benchmark design
4. Human evaluation processes
5. Hallucination assessment
6. Performance validation techniques

Module 9: Infrastructure and Training Operations

1. GPU and hardware considerations
2. Distributed training concepts
3. Resource optimization
4. Experiment tracking
5. Model versioning
6. Operational workflows

Module 10: Deployment and LLMOps

1. Model packaging strategies
2. Deployment architectures
3. Serving fine-tuned models
4. Monitoring and observability
5. Cost management
6. Continuous improvement processes

Module 11: Security, Governance and Compliance

1. Data privacy considerations
2. Secure training environments
3. AI governance requirements
4. Compliance obligations
5. Risk management practices
6. Responsible AI principles

Module 12: Fine-Tuning Project Workshop

1. Dataset preparation exercises
2. Supervised fine-tuning implementation
3. LoRA and QLoRA laboratories
4. Evaluation and benchmarking activities
5. Deployment and monitoring exercises
6. Final fine-tuning project using enterprise scenarios