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License: MIT License
contains all the evals not available at lm-eval-harness and layer wise inference for bigger models with GGUF support
License: MIT License
Continuity contains all the evals not available at lm-eval-harness and layer wise inference for bigger models with GGUF support one of the goals for this repository in the future would be to capitalize on MFU (model flops utlization) and the other to come up with newer evals in order to make the models humble again MFU = (observed token/s) / theoretical limit for decoder only models a good approximation for theoritical maximum is 6N (matmul flops/token) + 6LH(2QT) for self attetntion where L,H,Q and T stands for number of layers, number of heads, head dim and sequence length respec. The very first task would be to figure out how to get ~70% MFU How does transformers work? Following CUDAMORE tutorials and writing some triton kernels
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