Data Science, ML and Analytics Engineering

Deep dive into LLM Part Two

In the first part we discussed the practical part of deep dive into LLM.

In this part we will talk about key papers that will help in understanding LLM and passing interviews =) But more on that later.

It all starts with the first GPT

Then I recommend reading the paper about InstructGPT. The topic of training with feedback from a person is discussed there.

Then there are a couple of interesting papers:
Information Retrieval with Contrastive Learning

Then I recommend that you familiarize yourself with two truly iconic papers: LORA and QLORA, which solve the following problems:
– learning speed
– computing resources
– memory efficiency

Two more equally important paperpers are PPO and DPO. Understanding these works will help in reward modeling.

And finally:
Switch Transformers  – as a base Mixtures of experts
Mixtral of Experts  – as Open Source SOTA
Llama 2

Happy reading everyone

Deep dive into LLM Part One

I’ve started delving deeper into LLM, and personally, I find it much easier to dive myself through practice.

This way, one can grasp all the key concepts and outline a list of papers for further exploration.

I began with the StackLLaMA note: A hands-on guide to train LLaMA with RLHF

Here, you can immediately familiarize yourself with the concepts of Reinforcement Learning from Human Feedback, effective training with LoRA, PPO.

You’ll also get acquainted with the Hugging Face library zoo: accelerate, bitsandbytes, peft, and trl.

The note uses the StackExchange dataset, but for variety, I can recommend using the Anthropic/hh-rlhf dataset

In the second part, we’ll go through key papers.