Teaching LLM from scratch is challenging because of the extensive time required to understand why fine-tuned models fail; iteration cycles for fine-tuning on small datasets are typically measured in months. In contrast, the tuning iterations for a prompt take place in seconds, but after a few hours, performance levels off. The gigabytes of data in a warehouse cannot be squeezed into the prompt’s space.
Using only a few lines of code from the Lamini library, any developer, not just those skilled in machine learning, can train high-performing LLMs that are on par with…
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