Visão Geral

Este curso aborda as principais técnicas de Parameter-Efficient Fine-Tuning (PEFT), com foco especial em LoRA (Low-Rank Adaptation) e suas variações modernas. O participante aprenderá como adaptar Large Language Models (LLMs) de forma eficiente, reduzindo significativamente os requisitos de hardware, memória e custo computacional em comparação ao Fine-Tuning tradicional. O curso explora os fundamentos teóricos, implementação prática, avaliação de desempenho e estratégias de implantação corporativa para modelos ajustados utilizando PEFT.

Objetivo

Após realizar este curso, você será capaz de:

• Compreender os fundamentos do Parameter-Efficient Fine-Tuning (PEFT)
• Entender o funcionamento matemático e arquitetural do LoRA
• Implementar adaptações eficientes em Large Language Models
• Comparar diferentes técnicas de PEFT e selecionar a mais adequada para cada cenário
• Avaliar desempenho, custo e qualidade de modelos ajustados
• Implantar e gerenciar modelos customizados utilizando LoRA e técnicas relacionadas

Publico Alvo

• Engenheiros de Machine Learning
• Engenheiros de IA Generativa
• Cientistas de Dados
• Desenvolvedores de aplicações baseadas em LLMs
• Arquitetos de IA e soluções inteligentes
• Pesquisadores interessados em customização eficiente de modelos

Pre-Requisitos

• Conhecimentos de Machine Learning e Deep Learning
• Familiaridade com Python
• Conhecimentos básicos de Transformers e Large Language Models
• Noções de treinamento de modelos de IA são recomendadas

Conteúdo Programatico

Module 1: Introduction to Parameter-Efficient Fine-Tuning

1. Evolution of model customization techniques
2. Challenges of traditional fine-tuning
3. Fundamentals of PEFT
4. Benefits and trade-offs
5. Enterprise use cases
6. PEFT ecosystem overview

Module 2: Foundations of Large Language Models

1. Transformer architecture review
2. Language model training lifecycle
3. Model parameters and weights
4. Representation learning concepts
5. Inference workflows
6. LLM customization opportunities

Module 3: Mathematical Foundations for LoRA

1. Matrix decomposition fundamentals
2. Low-rank approximation concepts
3. Linear algebra for model adaptation
4. Parameter reduction techniques
5. Optimization principles
6. Computational efficiency considerations

Module 4: Understanding LoRA Architecture

1. LoRA design principles
2. Low-Rank Adaptation mechanisms
3. Trainable adapter matrices
4. Weight injection strategies
5. Architecture integration patterns
6. Performance characteristics

Module 5: Implementing LoRA Fine-Tuning

1. Environment preparation
2. Dataset preparation
3. LoRA configuration parameters
4. Training workflows
5. Hyperparameter selection
6. Fine-tuning best practices

Module 6: QLoRA and Memory Optimization

1. Quantization fundamentals
2. QLoRA architecture
3. 4-bit and low-precision techniques
4. Memory-efficient training
5. Hardware optimization strategies
6. Cost reduction approaches

Module 7: Advanced PEFT Techniques

1. Adapter-based tuning
2. Prefix tuning
3. Prompt tuning
4. P-Tuning methodologies
5. IA³ techniques
6. Comparative analysis of PEFT methods

Module 8: Evaluation and Benchmarking

1. Performance evaluation metrics
2. Accuracy assessment techniques
3. Benchmark creation
4. Quality validation methodologies
5. Resource utilization analysis
6. Comparative testing strategies

Module 9: Multi-Adapter and Domain Adaptation Strategies

1. Multi-domain adaptation
2. Adapter composition techniques
3. Task-specific customization
4. Domain specialization approaches
5. Modular adaptation architectures
6. Enterprise deployment scenarios

Module 10: Deployment and Operationalization

1. Model packaging and distribution
2. Adapter serving architectures
3. Runtime integration techniques
4. Monitoring and observability
5. Cost and performance optimization
6. Production deployment strategies

Module 11: Governance, Security and Responsible AI

1. Secure model customization
2. Data privacy considerations
3. AI governance requirements
4. Compliance and auditability
5. Risk management practices
6. Responsible AI principles

Module 12: PEFT and LoRA Workshop

1. LoRA implementation laboratory
2. QLoRA optimization exercises
3. Adapter comparison projects
4. Domain adaptation scenarios
5. Performance benchmarking activities
6. Final PEFT and LoRA enterprise project