What It Does
A transformer based language model implemented directly in PyTorch with pretrained GPT weights used to accelerate training. This provides full control over model architecture, training process, and deployment, allowing organisations to run AI systems entirely within their own infrastructure.
The Business Problem
In regulated environments or organisations handling sensitive data, sending information to external AI services is often not acceptable. Building and running models internally ensures full control over data handling, infrastructure, and long term operating costs.
What Was Built
Complete transformer architecture including attention layers, feedforward networks, residual connections, and layer normalisation
Multi head self attention and cross attention implemented directly in PyTorch
Pretrained GPT weight initialisation to accelerate training convergence
Custom training and evaluation loops using CUDA acceleration
Fully self contained deployment without reliance on external AI APIs
Business Impact
Removes dependency on external AI APIs
Enables full on premises deployment
Provides predictable long term infrastructure costs
Meets strict regulatory and data governance requirements
Allows models to be tailored to specific organisational needs