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Quick Start Guide to Large Language Models

Strategies and Best Practices for Using ChatGPT and Other LLMs

بقلم : Sinan Ozdemir 2024-06-05

Large language models (LLMs) are AI models that are usually derived from the Transformer architecture and are designed to understand and generate human language, code, and more. 
LLMs are trained on vast amounts of text data and massive datasets, allowing them to capture the complexities and nuances of human language. 
LLMs can perform a wide range of language-related tasks, from simple text classification to text generation, with high accuracy, fluency, and style. 
In the healthcare industry, LLMs are being used for electronic medical record (EMR) processing, clinical trial matching, and drug discovery. In finance, they are being utilized for fraud detection, sentiment analysis of financial news, and even trading strategies. LLMs are also used for customer service automation via chatbots and virtual assistants. Owing to their versatility and highly performant natures, Transformer-based LLMs are becoming an increasingly valuable assets in a variety of industries and applications.
Language modelling is a subfield of NLP that involves the creation of statistical/deep learning models for predicting the likelihood of a sequence of tokens in a specified vocabulary (a limited and known set of tokens). 
A token is the smallest unit of semantic meaning, which is created by breaking down a sentence or piece of text into smaller units; it is the basic input for an LLM. The defining features of LLMs are their large size and large training datasets, which enable them to perform complex language tasks, such as text generation and classification, with high accuracy and with little to no fine-tuning.
Every LLM on the market has been pre-trained on a large corpus of datasets, text data and on specific language modelling-related tasks. During pre-training and machine training ML, the LLM tries to learn and understand general language and relationships between words. Every LLM is trained on different corpora and on different tasks. Depending on which LLM you decide to use, it will likely be pre-trained differently from the rest. This is what sets LLMs apart from each other. Some LLMs are trained on proprietary data sources, including OpenAI’s GPT family of models, to give their parent companies an edge over their competitors.
In Quick Start Guide to Large Language Models, Sinan Ozdemir talks about: What Are Large Language Models, Semantic Search with LLMs, First Steps with Prompt Engineering, Working with Prompts Across Models, Getting the Most Out of LLMs, Advanced Prompt Engineering, Customizing Embeddings and Model Architectures, Advanced LLM Usage, Moving Beyond Foundation Models, Advanced Open-Source LLM Fine-Turning, Moving LLMs into Production.
For those working with Arabic language content, the strategies and best practices outlined in this guide can also be highly valuable. Large language models are being increasingly applied to tasks involving the Arabic language, such as building chatbots, improving linguistic models, and enabling in-context translation. 
By leveraging the power of LLMs trained on high-quality Arabic datasets, content creators and platforms can unlock new capabilities for processing, generating, and understanding Arabic text with greater fluency and accuracy. 
Readers interested in applying these LLM techniques to Arabic language applications are encouraged to visit https://edara.com/home/ai which provides access to premium Arabic datasets and linguistic models, which can be fine-tuned and customized to meet specific needs. 
Building AI-powered solutions for the Arabic market requires specialized expertise, and this guide can serve as a helpful starting point for those looking to harness the potential of large language models within an Arabic language context.
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About the Author:
Sinan Ozdemir: is currently the founder and CTO of Shiba Technologies.

Book Info:
Title: Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs
Author: Sinan Ozdemir
Pages: 288
Publisher: Addison-Wesley Professional
ISBN: 978-0138199197