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repliedto AbstractPhil's post about 16 hours ago
Today, I'll be determining the codebook capacity and utility potential for the larger batteries; Fresnel, Johanna, Grandmaster, Freckles, and Johanna-F variants, which should give a good indication of which models are capable of handling codebooks and which are more errant. The earlier all use SVD while the later do not. The differences are noted per and the behavior divergent.
I anticipate the D=16 will be more errant, and the final-state variants of those could very well be much more difficult or costly to inference as their axis bends are likely considerably harder to track. However, I'm confident that enough bounces will give the yield required so I'll set up some high-yield noise barrages to determine how much of them we can in fact extract from Johanna, and then set up similar barrages for images to map the internals of Fresnel and Grandmaster.
Grandmaster will be tricky, as it was an experimental Johanna-256 finetuned series meant to map sigma noised image inputs to recreate Fresnel behavioral output. Noised image goes in -> Fresnel-grade replication comes out in high res.
This allowed preliminary Dall-E Mini-esque VAE generation and will be explored further for the stereoscopic translation subsystem, to allow image generation in the unique format of diffusion that I was working out. I anticipate this system to be more than capable at making monstrosities, so I won't be posting TOO MANY prelims on this one, but the high-capacity potential of these noise makers are meaningfully powerful. Getting uniform codebooks in-place for these models will allow full transformer mapping downstream instead of just guess working the MSE piecemeal, which the earlier versions and variants were doing.
I'm straying from the CLS specifically for this series because CLS creates adjudicated pools of bias orbiting the INCORRECT orbiter some SVAE. The orbital target IS the soft-hand accumulated bias with the sphere-norm, so having a competitor isn't going to be a good option. repliedto ajibawa-2023's post about 2 months ago
Cpp-Code-Large
Dataset: https://huggingface.co/datasets/ajibawa-2023/Cpp-Code-Large
Cpp-Code-Large is a large-scale corpus of C++ source code comprising more than 5 million lines of C++ code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and static program analysis for the C++ ecosystem.
By providing a high-volume, language-specific corpus, Cpp-Code-Large enables systematic experimentation in C++-focused model training, domain adaptation, and downstream code understanding tasks.
Cpp-Code-Large addresses the need for a dedicated C++-only dataset at substantial scale, enabling focused research across systems programming, performance-critical applications, embedded systems, game engines, and large-scale native software projects.Organizations
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