Custom Fine-Tuning of Language Models with Lamini

This project showcases the fine-tuning capabilities of Lamini's language model engine to adapt large language models (LLMs) for specific use cases and domains. Using Lamini's API and custom dataset, the project refines the "Meta-Llama-3-8B-Instruct" model to answer user-specific queries with optimized accuracy. By leveraging Lamini’s fine-tuning parameters such as learning rate, early stopping, and max training steps, this project demonstrates how to enhance model performance through precise adjustments to meet particular application needs.

Key Features:

1. Data Customization and Preprocessing: Utilizes a diverse set of Q&A data to adapt the language model’s responses for specific question types.
2. Flexible Hyperparameter Tuning: Adjusts parameters like learning rate and training steps to control the model's learning process, ensuring balanced performance and efficiency.
3. Enhanced User-Specific Adaptability: Fine-tunes the language model to align closely with the user’s domain or business needs, making it more responsive to specialized queries.
4. Lamini Platform Integration: Uses Lamini’s language model engine API for fine-tuning, supporting efficient customization with minimal hardware requirements.

This project is ideal for users seeking to enhance LLMs for applications in customer support, documentation assistance, or domain-specific knowledge bases. It demonstrates how Lamini’s fine-tuning capabilities streamline model adaptation, creating responsive AI solutions tailored to distinct industry needs.