Introduction

Modeling the compute versus performance tradeoff of many open LLMs.
This is AI generated audio with Python and 11Labs.
Source code: https://github.com/natolambert/interconnects-tools
Original post: https://www.interconnects.ai/p/compute-efficient-open-llms

0:00 The end of the "best open LLM"
3:05 Compute efficient open LLMs

Fig 1: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/scaling/img_004.jpeg
Fig 2: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/scaling/img_009.png
Fig 3: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/scaling/img_014.png
Fig 4: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/scaling/img_016.png
Fig 5: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/scaling/img_018.png
Fig 6: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/scaling/img_020.png
Fig 7: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/scaling/img_022.png
Fig 8: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/scaling/img_024.png
Fig 9: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/scaling/img_028.png

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.interconnects.ai/subscribe