Fine-tuning an LLM has become a popular technique to tailor it to specific requirements.
Just as a human, with cognitive capabilities and NLP skills learned during growth, may have limited knowledge on certain subjects unless studied, an LLM can generate responses to general topics but requires fine-tuning for in-depth knowledge in a specific field, much like how professionals acquire expertise through specialized education.
Let\’s explore how LLMs are fine-tuned:
Instruction Fine-Tuning:
The simplest method involves training the model for customized responses by providing a highly detailed, task-specific prompt.
Full Fine-Tuning:
A resource-intensive task, it is financially expensive. Algorithms like PEFT and LoRA help reduce overall training costs.
Transfer Learning:
Retraining a model for a similar task, this process is remarkably fast.
RAG (Retrieval-Augmented Generation):
A cost-effective alternative to fine-tuning, RAG is preferred by clients for its simplicity and shorter training time.
While full fine-tuning is an option, none of my clients have opted for it so far.
Please share your experiences and what are the popular use cases you find for full fine tuning.
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