Introduction to LLMs

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| Artificial Intelligence |

In this tutorial, you will learn what a LLM (Large Language Model) is, how it works, and examples of its use in natural language processing applications.

By the end, you will have a clear understanding of what LLMs are, how they are trained, and how they can be integrated into AI projects.

1. What is an LLM?

A LLM (Large Language Model) is an artificial intelligence model designed to understand and generate natural language text. They are trained on massive amounts of textual data and learn patterns, semantic relationships, and language syntax.

  • Well-known LLM examples:

  • GPT (Generative Pre-trained Transformer)

  • LLaMA
  • BERT (Bidirectional Encoder Representations from Transformers)

2. How LLMs Work

LLMs operate using deep neural networks, usually of the Transformer type, which learn to predict the next word in a sentence based on context.

  • Main stages:

  • Pre-training: the model learns language patterns from large text corpora.

  • Fine-tuning: it specializes in specific tasks, such as translation, summarization, or question answering.
  • Inference: the model generates text or performs tasks based on instructions.

3. Common Applications of LLMs

  • Chatbots and virtual assistants: GPT, ChatGPT, Bard.
  • Content generation: articles, summaries, product descriptions.
  • Machine translation: real-time text translation.
  • Sentiment analysis: detecting emotions in social media text.
  • Intelligent search: improving search results with natural language understanding.

4. Advantages of LLMs

  • Ability to generate coherent and relevant text.
  • Adaptable to various language tasks.
  • Reduces human effort in writing or text analysis tasks.

5. Limitations of LLMs

  • May generate incorrect or biased information.
  • Require significant computational resources.
  • Do not possess true understanding; they only predict language patterns.

6. How to Start Using an LLM

  • Use APIs of existing models (OpenAI, Hugging Face, Cohere).
  • Integrate into applications via HTTP requests or official SDKs.
  • Test simple tasks like text generation, classification, or analysis.

With this tutorial, you now have a clear idea of what an LLM is and how to use it. In upcoming tutorials, we will dive deeper into fine-tuning, embeddings, and building LLM-based applications.

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