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flux-jl

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Amey-Thakur / JULIA

Sponsor
Star 9

50-Day Julia Programming Challenge by Amey Thakur and Mega Satish, focused on computational modeling, high-performance computing, and machine learning.

data-science machine-learning deep-learning julia julia-language high-performance-computing data-science-portfolio computational-science deep-learning-tutorial julialang julia-programming data-science-learning julia-programming-language data-science-team julia-lang 50-days-of-code ameythakur amey megasatish flux-jl
  • Updated Feb 21, 2026
  • Julia

zaydabash / ContinuumLM

Star 0

Neural ODE Transformer in Julia, trained via adjoint sensitivity methods. Matched-architecture vs. discrete Transformer on Penn Treebank: 119.9 val perplexity vs 113.7 discrete. Single-seed result; discrete wins at this scale as expected, continuous-depth advantage expected to emerge at larger scale.

machine-learning natural-language-processing deep-learning julia text-generation transformer neural-networks tensorboard differential-equations language-model continuous-time adjoint-method neural-ode sciml kv-cache autoregressive-generation flux-jl continuous-depth
  • Updated Jun 30, 2026
  • Julia

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