The Architecture of Intelligence

We train and fine-tune our models using a sophisticated blend of cutting-edge methodologies to ensure unparalleled performance, safety, and adaptability.

AI Training & Fine-Tuning Methods

How we build and refine our state-of-the-art models.

Supervised Fine-Tuning (SFT)

We refine base models using high-quality, labeled datasets. This process teaches the AI to follow specific instructions and produce accurate, relevant outputs for specialized tasks.

Reinforcement Learning (RLHF)

By incorporating human feedback, our models learn to align with complex human values. This iterative process rewards desired behaviors, making our AI safer and more helpful.

Unsupervised Pre-training

Our models are initially trained on vast amounts of unlabeled data, allowing them to discover underlying patterns, structures, and semantic relationships without human guidance.

Transfer Learning

We leverage knowledge from powerful foundation models and adapt it to new domains. This dramatically reduces training time and improves performance on niche tasks.

Efficient Fine-Tuning (LoRA)

Using techniques like Low-Rank Adaptation, we efficiently tune massive models with a fraction of the computational cost, enabling rapid iteration and customization.

Generative Adversarial Networks

We use a "generator" and "discriminator" system that compete to produce highly realistic and novel outputs, pushing the boundaries of creative and synthetic data generation.