Training

Training is the process of teaching an AI model by showing it examples and adjusting its parameters to improve performance. For language models, this involves learning to predict text from massive datasets.

Training stages for LLMs:

1. Pre-training: Learning general language patterns from vast text data
2. Fine-tuning: Adapting to specific tasks or domains
3. RLHF: Aligning behaviour with human preferences
4. Continued training: Ongoing improvements and updates

What happens during training:

• Model processes training examples
• Compares predictions to actual targets
• Calculates error (loss)
• Backpropagates error to adjust weights
• Repeats billions of times

Training requirements for LLMs:

• Data: Trillions of tokens of text
• Compute: Thousands of GPUs for months
• Cost: $10M-$100M+ for frontier models
• Expertise: Large teams of ML engineers

This is why most businesses use pre-trained models rather than training from scratch. Fine-tuning offers a middle ground - smaller data and compute requirements to specialise an existing model.