Fine-Tuning Large Language Models Certification

Module 1: Introduction to LLM Fine-Tuning

• Understanding fine-tuning
• Pre-training versus fine-tuning
• Fine-tuning use cases
• Model customization
• Fine-tuning workflow

Module 2: Large Language Model Fundamentals

• Transformer architecture
• Tokens and tokenization
• Context windows
• Model parameters
• Language-model prediction

Module 3: Understanding Model Adaptation

• Prompting versus fine-tuning
• Retrieval versus fine-tuning
• Full fine-tuning
• Parameter-efficient adaptation
• Selecting an adaptation strategy

Module 4: Selecting a Base Model

• Model capabilities
• Model size
• Context requirements
• Computational requirements
• Use-case alignment

Module 5: Fine-Tuning Infrastructure

• Compute requirements
• GPUs and accelerators
• Memory requirements
• Storage considerations
• Training environments

Module 6: Training Data Fundamentals

• Training examples
• Input-output pairs
• Instruction datasets
• Domain-specific data
• Dataset representativeness

Module 7: Data Collection

• Identifying data sources
• Collecting domain data
• Data permissions
• Data relevance
• Building training datasets

Module 8: Data Cleaning

• Removing duplicates
• Correcting formatting
• Handling missing information
• Filtering low-quality examples
• Normalizing datasets

Module 9: Data Formatting

• Instruction-response formats
• Conversational formats
• System and user messages
• Structured examples
• Dataset consistency

Module 10: Data Quality Management

• Accuracy
• Diversity
• Consistency
• Coverage
• Identifying problematic examples

Module 11: Creating Training, Validation, and Test Sets

• Dataset splitting
• Training sets
• Validation sets
• Test sets
• Preventing data leakage

Module 12: Tokenization for Fine-Tuning

• Tokenizer fundamentals
• Encoding training data
• Sequence lengths
• Padding
• Truncation

Module 13: Supervised Fine-Tuning

• SFT concepts
• Instruction-response learning
• Training objectives
• Training loops
• Model adaptation

Module 14: Instruction Tuning

• Instruction-following behaviour
• Designing instructions
• Task diversity
• Response quality
• Building instruction datasets

Module 15: Conversational Fine-Tuning

• Multi-turn conversations
• Role-based messages
• Conversation history
• Dialogue datasets
• Training conversational behaviour

Module 16: Domain-Specific Fine-Tuning

• Domain terminology
• Specialized writing styles
• Task-specific behaviour
• Domain datasets
• Evaluating specialization

Module 17: Full-Parameter Fine-Tuning

• Updating model parameters
• Computational requirements
• Training stability
• Resource considerations
• Appropriate use cases

Module 18: Parameter-Efficient Fine-Tuning

• PEFT concepts
• Reducing trainable parameters
• Memory efficiency
• Training efficiency
• PEFT use cases

Module 19: LoRA Fundamentals

• Low-rank adaptation
• Adapter matrices
• Rank selection
• Target modules
• Training LoRA adapters

Module 20: Quantized Fine-Tuning

• Quantization concepts
• Reduced precision
• Memory-efficient training
• Quantized base models
• Quality considerations

Module 21: Adapter-Based Model Customization

• Adapter concepts
• Training adapters
• Managing multiple adapters
• Domain-specific adapters
• Adapter deployment

Module 22: Hyperparameter Configuration

• Learning rate
• Batch size
• Epochs
• Sequence length
• Gradient accumulation

Module 23: Optimizers and Learning Rate Scheduling

• Optimization concepts
• Gradient updates
• Optimizer selection
• Learning rate schedules
• Training stability

Module 24: Managing GPU Memory

• Memory consumption
• Mixed precision
• Gradient accumulation
• Gradient checkpointing
• Memory optimization

Module 25: Monitoring Training

• Training loss
• Validation loss
• Learning curves
• Training stability
• Identifying abnormal behaviour

Module 26: Overfitting and Underfitting

• Recognizing overfitting
• Recognizing underfitting
• Dataset considerations
• Training duration
• Regularization approaches

Module 27: Model Evaluation

• Evaluation datasets
• Task-specific metrics
• Response quality
• Behavioural evaluation
• Comparing model versions

Module 28: Automated Evaluation

• Evaluation pipelines
• Benchmark datasets
• Exact-match metrics
• Similarity-based metrics
• Reproducible evaluation

Module 29: Human Evaluation

• Evaluation criteria
• Response comparison
• Quality rubrics
• Reviewer consistency
• Analyzing human feedback

Module 30: Preference Data

• Preferred and rejected responses
• Collecting preferences
• Ranking outputs
• Data quality
• Preference dataset preparation

Module 31: Preference Optimization

• Preference-learning concepts
• Aligning model behaviour
• Training from preference pairs
• Evaluating behavioural changes
• Managing trade-offs

Module 32: Fine-Tuning for Structured Outputs

• Structured response formats
• JSON-style outputs
• Schema consistency
• Training structured examples
• Validating generated outputs

Module 33: Fine-Tuning for Tool Use

• Tool-use datasets
• Function selection
• Argument generation
• Tool-result handling
• Evaluating tool behaviour

Module 34: Fine-Tuning for Specialized Assistants

• Defining assistant behaviour
• Domain instructions
• Conversation examples
• Response standards
• Evaluating assistant performance

Module 35: Safety and Responsible Fine-Tuning

• Dataset risks
• Harmful training examples
• Bias considerations
• Sensitive information
• Behavioural safeguards

Module 36: Data Privacy in Fine-Tuning

• Personal information
• Sensitive datasets
• Data minimization
• Access controls
• Secure data handling

Module 37: Experiment Tracking

• Recording configurations
• Dataset versions
• Training metrics
• Model versions
• Comparing experiments

Module 38: Reproducible Training Workflows

• Configuration management
• Random seeds
• Environment tracking
• Dataset versioning
• Reproducing results

Module 39: Model Checkpoints

• Saving checkpoints
• Checkpoint frequency
• Restoring training
• Comparing checkpoints
• Storage management

Module 40: Evaluating Fine-Tuned Models Against Base Models

• Establishing baselines
• Side-by-side comparison
• Task performance
• Behavioural differences
• Identifying regressions

Module 41: Fine-Tuning Debugging

• Poor training data
• Incorrect formatting
• Unstable loss
• Unexpected outputs
• Diagnosing performance problems

Module 42: Improving Fine-Tuning Results

• Improving datasets
• Adjusting hyperparameters
• Refining training examples
• Balancing data
• Iterative experimentation

Module 43: Model Merging Concepts

• Adapter merging
• Combining model changes
• Merge considerations
• Evaluating merged models
• Version management

Module 44: Inference Optimization

• Model loading
• Quantization
• Batching
• Response latency
• Resource utilization

Module 45: Deploying Fine-Tuned Models

• Model packaging
• Serving architecture
• API endpoints
• Environment configuration
• Deployment workflows

Module 46: Scaling Model Inference

• Concurrent requests
• Dynamic batching
• GPU utilization
• Load management
• Scaling infrastructure

Module 47: Monitoring Fine-Tuned Models

• Production performance
• Output quality
• Behaviour changes
• Data drift
• Feedback collection

Module 48: Building an End-to-End Fine-Tuning Workflow

• Defining the use case
• Preparing the dataset
• Selecting the training approach
• Evaluating the model
• Planning deployment