In Short:
This presentation discusses the development cycle of Large Language Models (LLMs), covering architectural implementation, finetuning stages, evaluation methods, and caveats. It includes topics like using LLMs, datasets, tokenization, architecture, finetuning, and evaluation rules. The presentation is in video format with clickable chapter marks for easy navigation. The speaker may create more video content in the future if this format is well-received.
If your weekend plans include catching up on AI developments and understanding Large Language Models (LLMs), I’ve prepared a 1-hour presentation on the development cycle of LLMs. The presentation covers everything from architectural implementation to the fine-tuning stages.
Overview of the Presentation
The presentation also includes an overview and discussion of the different ways LLMs are evaluated, along with the caveats of each method.
Below, you’ll find a table of contents to get an idea of what this video covers. The video itself has clickable chapter marks, allowing you to jump directly to topics of interest:
- 00:00 – Using LLMs
- 02:50 – The stages of developing an LLM
- 05:26 – The dataset
- 10:15 – Generating multi-word outputs
- 12:30 – Tokenization
- 15:35 – Pretraining datasets
- 21:53 – LLM architecture
- 27:20 – Pretraining
- 35:21 – Classification finetuning
- 39:48 – Instruction finetuning
- 43:06 – Preference finetuning
- 46:04 – Evaluating LLMs
- 53:59 – Pretraining & finetuning rules of thumb
This presentation is a slight departure from my usual text-based content. If you find this format useful and informative, I might occasionally create and share more videos like this in the future.
Happy viewing!
