Lesson 4
31. And why do they need training at all?
32. Large language models refer to large, general-purpose language models that can be pre-trained and then fine-tuned for specific purposes.
33.In this context, large refers to: The size of the training dataset, which can sometimes be at the petabyte scale
34. And the number of parameters.
35. Parameters are the memories and knowledge that the machine has learned during model training.
35. They determine the ability of a model to solve a problem, such as predicting text, and can reach billions or even trillions in size.
36. General-purpose means that the models can sufficiently solve common problems
37. This is thanks to the commonality of a human language, regardless of the specific tasks.
38. Saying LLMs are pre-trained and fine-tuned, means… …that they have been pre-trained for a general
39. purpose with a large dataset… ...and then fine-tuned for specific goals with a much smaller dataset.
40. But how are LLMs trained?
To be continued…check post 4