A developer's guide to open source LLMs and generative AI

The integration of AI with open-source platforms has led to a surge in generative AI projects, with over 8,000 initiatives on GitHub, including commercially backed models like Meta’s LLaMA and various experimental applications. Open-source large language models (LLMs) offer transparency and rapid development due to community contributions, contrasting with closed-source models that are more secure and user-friendly. Fine-tuning open-source LLMs on cloud platforms like AWS or Azure can enhance their performance in specific applications, using techniques like Microsoft's LoRA to improve efficiency and reduce processing time. The open-source LLM landscape includes notable models such as OpenLLaMA, Falcon-Series, MPT-Series, and FastChat-T5, each tailored for distinct tasks and applications. The future of open-source LLMs looks promising, with potential for local deployment and reduced infrastructure needs, although financial support remains a challenge. Despite predictions, the fundamental algorithms of generative AI have remained simple, with scalability being the primary focus. This evolving field holds the potential to significantly transform the developer landscape, with endless possibilities for innovation and application.