When to use fine-tuning?
When we build an LLM application the first step is to select an appropriate pre-trained or foundation model suitable for our use case. Once the base model is selected we should try prompt engineering to quickly see whether the model fits our use case realistically or not and evaluate the performance of the base model on our use case.
In case with prompt engineering we are not able to achieve a reasonable level of performance we should proceed with fine-tuning. Fine-tuning should be done when we want the model to specialize for a particular task or set of tasks and have a labeled unbiased diverse dataset available. It is also advisable to do fine-tuning for domain-specific adoption like learning medical law or finance language.
Fine Tuning Large Language Model (LLM)
Large Language Models (LLMs) have revolutionized the natural language processing by excelling in tasks such as text generation, translation, summarization and question answering. Despite their impressive capabilities, these models may not always be suitable for specific tasks or domains due to compatibility issues. To overcome this fine tuning is performed. Fine tuning allows the users to customize pre-trained language models for specialized tasks. This involves refining the model on a limited dataset of task-specific information, enhancing its performance in that particular task while retaining its overall language proficiency.
Table of Content
- What is Fine Tuning?
- Why Fine-tune?
- Types of Fine Tuning
- Prompt Engineering vs RAG vs Fine tuning.
- When to use fine-tuning?
- How is fine-tuning performed?
- Fine Tuning Large Language Model Implementation
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