LLM Researcher and Scientist Roadmap: A Guide to Mastering Large Language Models Research

This comprehensive article serves as a roadmap for aspiring LLM researchers and scientists, offering a step-by-step guide to mastering the intricacies of Large Language Models (LLMs) to take your first step as a researcher in this field.  

The content unfolds with an exploration of the LLM architecture, providing insights into its foundational structure. Subsequent sections delve into crucial aspects such as constructing an instruction dataset, harnessing pre-trained LLM models, supervised fine-tuning, reinforcement learning from human feedback, and the evaluation process. 

Additionally, the article delves into advanced optimization techniques, covering quantization and inference optimization. By navigating through the detailed Table of Contents, readers gain a thorough understanding of the essential components involved in LLM research, empowering them to embark on a journey toward expertise in the field.

Table of Contents:

1. LLM Architecture

2. Building an Instruction Dataset

3. Pre-Trained LLM Models

4. Supervised Fine-Tuning

5. Reinforcement Learning from Human Feedback

6. LLM Evaluation

7. LLM Quantization

8. Inference Optimization

This article is inspired and build upon the Large Language Model Course GitHub repository by Maxime Labonne and Pietro Monticone

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