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The History of Open-Source LLMs: Better Base Models (Part Two)

Jul 31, 2023 - cameronrwolfe.substack.com
The article discusses the evolution of open-source Large Language Models (LLMs), focusing on how they have improved over time and the factors that have contributed to their success. Initially, open-source LLMs struggled with poor performance and heavy criticism. However, research has led to the creation of high-performing pre-trained LLMs that are now widely used and studied. The article highlights several models, including LLaMA, MPT, Falcon, and LLaMA-2, which have significantly improved the performance of open-source LLMs. These models have been pre-trained over massive datasets and have been optimized for fast and easy inference, making them more accessible and practical for use in both research and commercial applications.

The article also emphasizes the importance of the quality and composition of the pre-training dataset in achieving high performance. For instance, MPT models have increased the proportion of code in their training datasets, improving their performance on coding-based tasks. Falcon models have proposed a new pipeline for constructing high-quality corpora of text from the web. LLaMA-2 models have used an updated data pipeline and mix for pre-training, emphasizing factual sources to increase knowledge and reduce hallucinations. The article concludes by noting that the availability of high-quality base models has significantly contributed to the rise in popularity of open-source LLMs.

Key takeaways:

  • The article discusses the evolution of open-source Large Language Models (LLMs), focusing on the improvements in their performance and the factors that contributed to these improvements.
  • Recent open-source LLMs such as LLaMA, MPT, Falcon, and LLaMA-2 have significantly improved performance compared to their predecessors, primarily due to the use of larger, higher-quality datasets for pre-training.
  • These models also focus on inference efficiency, adopting various architectural modifications to speed up the inference process, making them more practical for commercial applications.
  • Despite the improvements, open-source LLMs still lag behind proprietary models in terms of performance. However, they offer advantages such as the ability to be fine-tuned on domain-specific data and lower deployment costs.
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