Speeding up inference without compromising quality
Speeding up inference while maintaining quality is a key challenge in deploying LLMs effectively, especially in real-time applications. The techniques mentioned, distillation and optimized algorithms, are just part of a broader suite of strategies that can be employed to this end. Let’s take a deeper dive into these and other methods.
Distillation
Distillation in the context of machine learning, particularly for LLMs, is a technique that helps in transferring knowledge from a larger, more complex model to a smaller, more efficient one. This process not only makes a model more deployable but also often retains a significant amount of the larger model’s accuracy. Let’s take an in-depth look at the various distillation techniques:
- Soft target distillation:
- Knowledge transfer: Soft target distillation transfers the “knowledge” encoded in the probability distributions of a larger model’...
