Fine-tuning a large language model (LLM) refers to the process of further
training a pre-trained model on a specific task or domain. This allows the
fine-tuned model to use its broad pre-training foundation and specialize it for
your specific task.
Adjust the language model’s output to match specific writing styles or tones
required for different applications like corporate communication, technical
documentation, customer service responses, and creative writing.
Improving Output Structure
Teach the model to produce consistent formatting or output structures like
JSON/XML generation, structured data extraction, API response formatting, and
report generation.
Handling Edge Cases
Refine the model to effectively address various exceptional scenarios like
domain-specific terminology, uncommon data formats, special use cases, and
error handling.
Domain Specialization
Adapt the model to excel in specific fields like medical diagnosis, legal analysis,
financial forecasting, or scientific research by incorporating domain-specific
knowledge and terminology.
Multilingual Adaptation
Enhance the model’s capabilities across different languages, including handling
cultural nuances, idioms, and region-specific expressions for global applications.
Safety & Alignment
Train the model to follow safety guidelines, ethical principles, and alignment
requirements while maintaining helpfulness and avoiding harmful outputs.