Large Language Models (LLMs)

1. Overview

Large Language Models (LLMs) are AI models designed to understand and generate human language. They are built using transformer architectures and trained on massive datasets.

2. Key Concepts

• Tokenization: Breaking text into smaller units (tokens).
• Attention Mechanism: Allowing the model to focus on different parts of the input sequence.
• Pre-training & Fine-tuning:
  • Pre-training: Learning general language patterns.
  • Fine-tuning: Adapting to specific tasks (e.g., chat, coding).

3. Popular Models

• GPT-4 (OpenAI): Proprietary, multimodal.
• Llama 3 (Meta): Open weights, highly efficient.
• Claude 3 (Anthropic): Strong reasoning and large context window.
• Gemini (Google): Multimodal, integrated with Google ecosystem.

4. Prompt Engineering

Techniques to guide LLM outputs:

• Zero-shot: Asking without examples.
• Few-shot: Providing a few examples of desired output.
• Chain-of-Thought: Encouraging the model to explain its reasoning.

5. Coding with LLMs (LangChain Example)

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?",
)

print(llm(prompt.format(product="colorful socks")))