Training Data Insights
Training datasets significantly impact model quality, with a recommendation of at least 20 times more data than model parameters. The pre-training phase utilizes vast datasets, like the new 1.5 trillion token set, which is three times larger than previous datasets. Fine-tuning through reinforcement learning from human feedback further refines outputs, elevating models from GPT-3 to GPT-4 caliber.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
678: StableLM: Open-source "ChatGPT"-like LLMs you can fit on one GPU — with @JonKrohnLearns
Related Questions
How are large language models (LLMs) trained, as discussed in the episode 670: LLaMA: GPT-3 performance, 10x smaller — with Jon Krohn (@JonKrohnLearns)?
What is the main topic of the clip Training Data Insights from the episode 678: StableLM: Open-source "ChatGPT"-like LLMs you can fit on one GPU — with @JonKrohnLearns?
How do the parameters of Cerebras GPT models compare to other large language models like Llama and Alpaca in the episode 676: The Chinchilla Scaling Laws — with Jon Krohn (@JonKrohnLearns) and the clip Cerebras GPT Unveiled?