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Fine-Tuned Model Pattern

A specialization pattern where a pre-trained Large Language Model is further trained on domain-specific data to improve performance for particular tasks or domains. This pattern creates specialized models that understand domain-specific terminology, follow specific formats, or exhibit particular behaviors.

When to Use

Use this pattern when you need models to understand specialized domain knowledge, follow specific output formats consistently, or when RAG doesn't provide sufficient performance improvements. Ideal for highly specialized domains like legal, medical, or technical fields where domain expertise is critical and consistent behavior is required.

Architecture Drawing

The architecture illustrates the fine-tuning pipeline from data preparation through model training to deployment, with ongoing monitoring and evaluation components.

Components: - Training Data Repository (100, 50) - Data Preprocessing Service (250, 50) - Base Model Registry (400, 50) - Fine-Tuning Engine (250, 150) - Model Validation Service (400, 150) - Model Registry (550, 150) - Inference Service (400, 250) - Performance Monitor (550, 250) - User Applications (250, 300)

Edges: - Training Data Repository → Data Preprocessing Service: "Raw Training Data" - Data Preprocessing Service → Fine-Tuning Engine: "Processed Dataset" - Base Model Registry → Fine-Tuning Engine: "Pre-trained Model" - Fine-Tuning Engine → Model Validation Service: "Candidate Model" - Model Validation Service → Model Registry: "Validated Model" - Model Registry → Inference Service: "Deployed Model" - Inference Service → User Applications: "Model Predictions" - Performance Monitor → Model Registry: "Performance Metrics" - User Applications → Performance Monitor: "Usage Analytics"

RAG Pattern, Model Versioning Pattern, A/B Testing Pattern

References

  1. Parameter-Efficient Fine-Tuning of Large Language Models - 2021 - arXiv - Comprehensive survey of efficient fine-tuning methods including LoRA and prefix tuning
  2. Fine-Tuning Language Models from Human Preferences - 2019 - arXiv - Methodology for fine-tuning models using human feedback and preference learning
  3. Domain-Adaptive Fine-Tuning for Neural Machine Translation - 2020 - ACL - Best practices for adapting pre-trained models to specific domains and tasks