Here is why Open Source LLMs are the Future of Generative AI

Course Q&A Generative AI with Large Language Models GenAI with LLMs Resources
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Open Source LLMs are the Future of Generative AI

As we can see in recent news, and as we have learnt from the course, Generative AI has reshaped numerous sectors with its capability to generate fresh, coherent content. As Large Language Models (LLMs) like OpenAI’s GPT series lead this revolution, it’s evident that open-source LLMs will be at the forefront. Here’s why:

  1. Democratization & Rapid Innovation: Open source stands for democratizing technology. Making LLMs open allows a vast community of developers and researchers to access and improve these models, leading to faster advancements and ensuring that the benefits of AI are available to all.
    Here are the top trending Github repos in August. Note the dominance of Open Source projects around LLMs (MetaGPT, llama and related)

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  2. Trust & Transparency: The “black box” nature of AI raises concerns. Open-source models, being transparent, allow users to inspect and understand the underlying algorithms, fostering trust, especially in critical sectors.
    Have you heard Samsung leaked secrets to OpenAI? This would not have happened with self-hosted Open Source LLMs (or being more careful and opting-out for logging using Azure OpenAI service, for example)

  1. Customization & Flexibility: Open source LLMs offer unmatched adaptability. Developers can modify these models for specific applications, ensuring relevant and precise outputs that may not be achievable with proprietary models.

Meta released Code Llama on August 24th

Just few days later, a fine-tuned variant of Code Llama from the Open Source community already reached better results, surpasing even GPT4 on the HumanEval benchmark.

  1. Collaborative Learning & Ethical Oversight: Open sourcing paves the way for global collaboration, bringing diverse perspectives that make models comprehensive and less biased. Moreover, the public can scrutinize and discuss potential biases and societal impacts, leading to the development of more ethically-conscious AI. If you have done the week 3 of the course, you know what is this about and why the alignment problem is critical (even for human existance!)

In essence, open-source LLMs are poised to dominate the generative AI landscape, promising a future where AI is more innovative, inclusive, and ethically grounded.

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This is very interesting, thank you for sharing! Now I have some homework to read that stuff

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How does Code Llama and other coding LLMs perform in real work coding tasks? I mean, the Benchmarks are usually not that representative of real-life use-cases, right?

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This is a very good question @deniz.aslan . I don’t have limited experience with Code Llama and its derivatives, and only via chat interface (no integration in VSCOde or similar like Github Copilot does). It is very difficult to say. The code it generates works generally, but this is not a quantifiable statement :grin: