Fine Tuning
What is Fine-tuning?
Fine-tuning an AI model is the process of taking a pre-trained, general-purpose model and further training it on a smaller, task-specific dataset to adapt it for a specialized purpose. This approach leverages the model's existing knowledge and adjusts its internal parameters to improve performance, accuracy, or new skills for a specific task, like medical text analysis or customer service. It's often more efficient than training a model from scratch, especially when you have limited data.
Problem Definition & Model Selection
We start by defining the exact task and performance objectives for your AI solution.
Once the scope and success metrics are clear, we identify and select the most suitable pre-trained model for the identified tasks and purpose.
Data Preparation & Structuring
We collect, clean, and preprocess a high-quality dataset tailored to your use case. This includes annotation, normalization, and splitting into training, validation, and testing sets, guaranteeing balanced, reliable model development.
Configuration & Fine-Tuning
We adjust the model architecture for your task, integrating methods like LoRA or PEFT to optimize performance. Our team carefully tunes hyper parameters like learning rate, batch size, epochs, etc.
Validation & Deployment
We rigorously test the fine-tuned model using relevant performance metrics before deployment. Once integrated into your environment, the model’s performance is continuously monitored to ensure its reliability.