Large language models (LLMs) are a class of artificial intelligence models primarily designed to understand and generate human-like text based on the input they receive. These models are trained on vast amounts of textual data and use different architectures and training strategies. Here are some of the major types of large language models and their distinguishing features:

1. Transformer Models:

   • Examples: GPT (Generative Pre-trained Transformer) series, BERT (Bidirectional Encoder Representations from Transformers).

   • Key Feature: They rely on the transformer architecture, which uses self-attention mechanisms to process words in relation to all other words in a sentence, rather than in sequential order. This allows them to be highly efficient in understanding context and generating text.

   • Differences:

     • GPT models are autoregressive, meaning they predict the next word in a sequence given all the previous words.

     • BERT models are auto-encoding, meaning they predict missing words in a sequence given surrounding words (useful for tasks like sentence completion and question answering).

2. Recurrent Neural Networks (RNNs):

• Examples: LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units).

• Key Feature: These models process text sequences by maintaining a form of memory of what has been processed, allowing them to maintain context over longer text sequences.

• Differences: Compared to transformers, RNNs are typically slower in processing since they handle data sequentially, but they can be very effective for smaller datasets and simpler tasks.

3. Evolutionary Models:

• Examples: Not as commonly cited in the context of LLMs, but evolutionary algorithms can be used to optimize neural network architectures, including language models.

• Key Feature: These models might evolve over time through methods that simulate natural selection to improve performance on specific tasks.

4. Hybrid Models:

• Examples: Models that combine elements of transformers with RNNs or other machine learning techniques.

• Key Feature: Aim to leverage the strengths of multiple architectures to improve efficiency, context understanding, or handling specific types of tasks.

5. Sparse Models:

• Examples: Models designed to use sparse activations (where only a subset of the model is active at a given time) to be more memory and compute-efficient.

• Key Feature: These models aim to maintain or improve performance while reducing computational cost and energy usage.

6. Diffusion Models for Text:

• Examples: Emerging area mostly known for image generation but being explored for text.

• Key Feature: These models iteratively refine text outputs from a noisy starting point, potentially leading to high-quality text generation with different characteristics compared to standard models.

Each of these types offers unique advantages and is suitable for different applications. Transformer models, particularly the GPT series, have been notably successful in generating coherent and contextually relevant text across a wide range of domains and are currently among the most widely used types of LLMs.

Public LLMs / Private LLMs

The landscape of large language models (LLMs) is often divided into public and private models, with each category having distinct characteristics in terms of accessibility, transparency, and usage.

1. Public Large Language Models

• Accessibility: Public LLMs are openly accessible to researchers, developers, and the general public either through APIs or by direct download of the model weights. Examples include OpenAI's GPT-2, which has been fully released to the public, and models like BERT and Gemini from Google.

• Transparency: These models often come with detailed documentation about their training data, methodologies, and performance metrics. This transparency helps in fostering a broader understanding and research into Artificial Intelligence (AI) and Neural Linguistic Programming (NLP).

• Community and Collaboration: Public models encourage collaboration across various sectors including academia, industry, and hobbyist AI practitioners. They are used as benchmarks in research and can spur innovation through shared challenges and solutions.

• Limitations and Regulations : Even though they are public, usage might still be bound by certain licenses and ethical guidelines to prevent misuse, such as generating misleading information or creating harmful content.

2. Private Large Language Models

• Accessibility: Private LLMs are held and used internally by companies or specific organizations. Access to these models, including their architecture and datasets, is restricted to internal users or specific partners. Examples include OpenAI's GPT-3 and GPT-4, which are available to the public through API access on a paid or restricted basis but without access to the model weights.

• Proprietary Development: These models are developed with proprietary data, resources, and research, often giving the owning company a competitive advantage. They might be tailored for specific applications that align with business goals, such as customer service bots, personalized recommendations, or automated content moderation.

• Commercial Application: Private LLMs are typically designed with commercial applications in mind. The owning companies control the usage scenarios, pricing models, and access permissions, which can lead to more polished and user-oriented products.

• Privacy and Security: Keeping the models private allows companies to implement strong control over how the models are used, helping to mitigate risks associated with misuse. However, this also means less external scrutiny regarding fairness, bias, and ethical implications.

The choice between public and private models depends on various factors including intended use, required scale, available resources, and ethical considerations. Public models promote wider knowledge dissemination and innovation, while private models allow for tailored solutions and controlled usage in sensitive or commercial environments. Each approach plays a crucial role in the development and application of AI technologies in society. 

Many cloud Customer Relationship Management (CRMs) allow you to bring your own LLM model.