Model Tuning

Model tuning is the process of optimizing a machine learning model’s hyperparameters—settings that define how the model learns from data but aren’t themselves learned during training—to maximize performance on specific tasks. This includes adjusting variables such as learning rates, batch size, and model architecture parameters, which control aspects of the training process and model structure. Tuning can involve strategies like manual adjustment, grid search, random search, or more sophisticated methods like Bayesian optimization. The ultimate goal is to improve a model’s accuracy, robustness, and generalization to new data, while mitigating issues like overfitting or underfitting

📄️ Model Tuning

The No Code Tuning feature allows users to fine-tune pre-deployed Text Generation and Code Generation models on the Qubrid AI platform—without writing a single line of code. Using an intuitive interface and user credits, users can train models with their own dataset and deploy them seamlessly on GPU instances.

📄️ User guide

This guide walks you through the process of fine-tuning large language models (LLMs) on the platform using a no-code interface, enabling custom model development without writing any code.