GenAI Different Types of LLM.

    Large language models (LLMs) can be classified by their purpose, how they are trained, or the type of data they use.
    Purpose
    Generic
    Predict the next word based on training data. These models are used for information retrieval.
    Instruction-tuned
    Predict responses to instructions. These models can generate text or code, or perform sentiment analysis.
    Dialog-tuned
    Predict the next response in a conversation. These models are used for chatbots and conversational AI.
    Training
    Zero-shot
    Trained on generic data to provide accurate results for general use cases. These models are ready for immediate use.
    Fine-tuned
    Trained further to enhance the effectiveness of the initial zero-shot model. These models are also called specialized LLMs.
    Data type
    Multimodal: Can handle both text and images. For example, GPT-4V can process and generate content in multiple modalities.
    Open source vs closed source
    Open source: Accessible to anyone, allowing users to inspect, modify, and use the code.
    Closed source: Not publicly accessible, and their usage is subject to the terms and conditions set by the organization or 
    company that owns them.
    
    Examples
    DistilBERT: A smaller, faster variant of BERT that's good for fast and efficient text analysis
    ELECTRA: Good for token classification and efficient pre-training
    PaLM: Good for translation, summarization, and question answering
    LLaMA: Good for AI-driven chatbots and content creation
    OpenAI Codex: An auto-completion programming tool built on GPT-3
    Vicuna: An open source LLM derived from Llama

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