Making a Vintage LLM from Scratch (crlf.link)

A tech enthusiast has successfully developed a vintage language model (LLM) trained exclusively on texts from the 1800s, highlighting both the challenges and triumphs of creating an AI from the ground up. The model, available on HuggingFace, boasts a 340 million parameter architecture based on Llama and encapsulates knowledge limited to the year 1900. This project involved the creation of custom scripts, data processing pipelines, and detailed dataset preparation. The author emphasizes their hands-on approach, reflecting on the significant learning curve and experimentation throughout the fourteen-week undertaking. This initiative is notable for the AI/ML community as it explores the potential of historical context in LLMs, providing a unique perspective on how language and societal norms have evolved. While the model is recognized as a hobbyist project, it serves as a compelling example of how constrained datasets can lead to rich, albeit imperfect, representations of language. The author notes that despite the model's inaccuracies—such as potential toxicity in generated content—the endeavor fosters deeper understanding of LLM training processes and the importance of historical datasets in shaping AI capabilities. This project could inspire further explorations into temporal LLMs and the historical accuracy of AI-generated content.