Fine-Tuning LLMs for Text Generation

6.

Deployment and Production Operations

6.1.

Model Inference Optimization

6.1.1.

Text Generation Strategies

6.1.1.1.

Decoding Methods

6.1.1.1.1.

Greedy Decoding

6.1.1.1.2.

6.1.1.1.2.1.

Beam Width Selection

6.1.1.1.2.2.

Length Normalization

6.1.1.1.2.3.

Coverage Penalties

6.1.1.1.3.

Sampling Techniques

6.1.1.1.3.1.

Temperature Scaling

6.1.1.1.3.2.

6.1.1.1.3.3.

6.1.1.1.3.4.

Typical Sampling

6.1.1.2.

Generation Control

6.1.1.2.1.

Length Constraints

6.1.1.2.2.

6.1.1.2.3.

Repetition Handling

6.1.1.3.

Batch Processing

6.1.1.3.1.

Parallel Generation

6.1.1.3.2.

Memory Management

6.1.1.3.3.

Throughput Optimization

6.1.2.

Performance Optimization

6.1.2.1.

Model Compression

6.1.2.1.1.

Quantization Techniques

6.1.2.1.1.1.

Post-Training Quantization

6.1.2.1.1.2.

Quantization-Aware Training

6.1.2.1.1.3.

Mixed Precision

6.1.2.1.2.

Pruning Methods

6.1.2.1.2.1.

Structured Pruning

6.1.2.1.2.2.

Unstructured Pruning

6.1.2.1.2.3.

Magnitude-Based Pruning

6.1.2.1.3.

Knowledge Distillation

6.1.2.1.3.1.

Teacher-Student Framework

6.1.2.1.3.2.

Distillation Objectives

6.1.2.1.3.3.

Performance Preservation

6.1.2.2.

Hardware Optimization

6.1.2.2.1.

GPU Utilization

6.1.2.2.2.

Memory Management

6.1.2.2.3.

Compute Optimization

6.2.

Serving Infrastructure

6.2.1.

API Development

6.2.1.1.

RESTful API Design

6.2.1.1.1.

Endpoint Structure

6.2.1.1.2.

Request/Response Format

6.2.1.1.3.

6.2.1.2.

Real-Time Serving

6.2.1.2.1.

Latency Optimization

6.2.1.2.2.

Concurrent Request Handling

6.2.1.2.3.

6.2.1.3.

Batch Processing Systems

6.2.1.3.1.

Queue Management

6.2.1.3.2.

Throughput Maximization

6.2.1.3.3.

Resource Scheduling

6.2.2.

Scalability Considerations

6.2.2.1.

Horizontal Scaling

6.2.2.1.1.

Load Distribution

6.2.2.1.2.

Service Replication

6.2.2.1.3.

Auto-Scaling Policies

6.2.2.2.

Caching Strategies

6.2.2.2.1.

Response Caching

6.2.2.2.2.

6.2.2.2.3.

Intermediate Result Caching

6.3.

Production Monitoring

6.3.1.

Performance Tracking

6.3.1.1.

Latency Monitoring

6.3.1.2.

Throughput Measurement

6.3.1.3.

Resource Utilization

6.3.1.4.

Error Rate Tracking

6.3.2.

Quality Monitoring

6.3.2.1.

Output Quality Assessment

6.3.2.1.1.

Automated Quality Checks

6.3.2.1.2.

Anomaly Detection

6.3.2.1.3.

Drift Monitoring

6.3.2.2.

User Feedback Integration

6.3.2.2.1.

Feedback Collection

6.3.2.2.2.

Quality Scoring

6.3.2.2.3.

Improvement Identification

6.3.3.

Maintenance and Updates

6.3.3.1.

Model Versioning

6.3.3.1.1.

Version Control Systems

6.3.3.1.2.

Rollback Procedures

6.3.3.1.3.

A/B Testing Infrastructure

6.3.3.2.

Retraining Pipelines

6.3.3.2.1.

Data Collection

6.3.3.2.2.

Automated Retraining

6.3.3.2.3.

Performance Validation

Previous

5. Evaluation and Quality Assessment

Go to top

Next

7. Advanced Techniques and Considerations