Summary
In this chapter, we covered the whole gamut of optimization techniques, primarily aimed at LLMs but generalizable to other foundational models and domains as well. The chapter was organized by the lifecycle of an LLM and different optimizations at each stage. We started off by covering improvements that can be achieved in the pre-training stage through data efficiencies and architectural improvements. We then covered optimization techniques related to the fine-tuning stage. Particularly, we talked about PEFT techniques like prompt tuning and reparameterization. The final category of improvements we covered was for the inference stage. Throughout the chapter, we also covered a number of worked-out examples to better understand the techniques.
We closed the chapter by covering emerging trends and research areas where we briefly touched upon alternate architectures, specialized hardware, and frameworks, as well as the emergence of task-specific small language models.
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