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🧱 Closed and Open-Source LLMs

Abstract

This section covers "Closed" and "Open-Source" LLMs briefly.

Closed and open-source LLMs refer to two different approaches to the development and distribution of LLMs. These models are designed to understand and generate human-like text and have a wide range of applications, including chatbots, content generation, translation, and more.

⚡️ Closed-Source LLMs

Closed-source models are developed and owned by a specific organization or company. Model architecture, training code and data are not made publicly available. Access to these models is typically restricted, and users need to pay for access.

Some of the popular examples of closed source LLMs are GPT-3, GPT-4, Gemini Pro, Claude etc. Here are the pros and cons of closed-source LLMs.

Pros:

  • Sophistication - Often larger and more advanced due to greater resources.
  • Polished features - Offer well-developed and ready-to-use capabilities.
  • Structured support - Benefit from dedicated support teams.

Cons:

  • Control - Users have limited control over model behavior and improvement.
  • Transparency - Lack of transparency regarding training data and potential biases.
  • Cost - Can be expensive to use, especially for large-scale applications.

⚡️ Open-Source LLMs

Open-source LLMs are developed with transparency in mind, and their code, architecture, and pretrained weights are made publicly available. Open-source LLMs encourage collaboration and innovation within the developer and research communities, enabling a wide range of applications and adaptations. These models are typically distributed under open-source licenses that allow developers and researchers to modify, extend, and build upon the model's capabilities freely.

Some of the popular examples of open-source LLMs are Llama2, Falcon, Mistral etc. Here are the pros and cons of open-source LLMs.

Pros:

  • Transparency - Transparency regarding training data, model architecture and code.
  • Customizability - Users can adapt models to specific needs and domains.
  • Collaboration - Benefit from collective expertise and knowledge sharing.
  • Cost - Often free to use, reducing financial barriers.

Cons:

  • Scale - May be smaller and less powerful than closed-source models due to resource constraints.
  • Maintenance - Reliant on community support for upkeep and updates.
  • Documentation - May have less comprehensive documentation and support materials.

In summary, closed-source large language models are proprietary, restricted, and often used for commercial purposes, while open-source models are openly accessible, encourage collaboration, and support a broader range of applications and research endeavors.

The choice between closed and open-source models depends on factors like accessibility, licensing, cost, and the specific use case or project requirements.