Fine-tuning one of the most powerful open source models, the text-generation task can be improved.

Article on Fine-tuning:

There's been a lot of buzz around the word 'Fine-tuning'. What exactly is it? Let me try to make it easier for you.

Fine-tuning Large Language Models (LLMs) involves retraining a pre-existing model on a smaller, domain-specific dataset. It's significant for saving computational resources and time compared to training from scratch. So, basically it is used to improve the model performance and overcome the challenges faced by LLMs. What are these challenges?

• Knowledge Cutoff - knowledge limited due to being trained on specific data.
• Hallucination - generating incorrect or misleading information.
• Black box Model - No proper interpretation of the model's outcome.

So the solutions to these problems are:

1. Prompt Engineering - providing examples in the form of prompts to guide the model's generation process.
2. Finetuning - Adjusting the parameters and training data of pre-trained LLMs for specific tasks or domains, enhancing accuracy and relevance for targeted applications.
3. RAG (Retreival Augmented Generation) - retrievies relevant knowledge from a knowledge base, resulting in more informed and contextually rich outputs.

Choosing any one out of the 3 solutions, depends on the specific requirements and objectives of the application.

Fine-tuning is particularly effective for modifying the behavior, writing style, or domain-specific knowledge of a model, especially when abundant, labeled training data specific to the domain is available, enabling a more tailored and precise adjustment of the model's behavior.

For example, imagine you have a pre-trained LLM designed for general language understanding, but you want to use it to generate product descriptions for an online marketplace. By fine-tuning the model with data specific to product descriptions, such as features, specifications, and customer reviews, you can enhance its ability to generate accurate and compelling descriptions tailored to the products on your platform.

Hope this short article helped you! Stay tuned for more.