--- title: "Deep and foundation models" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Deep and foundation models} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} resource_files: - figures/logo.svg - figures/logo-dark.svg --- ```{r setup, include=FALSE} library(hypermodR) knitr::opts_chunk$set(collapse = TRUE, comment = "#>", error = FALSE) ``` Model declarations do not download weights or import upstream Python. This example makes named classification specifications only; no pretrained inference or scientific result is produced. ```{r named-contracts} task <- hm_task("class", "classification", "image", "subject_id") constructors <- list( hm_htc, hm_hyperfree, hm_hypersigma, hm_hypervision, hm_dual_stream_mhsi, hm_spectralearth, hm_spectralgpt ) specifications <- lapply(constructors, function(f) f(task)) vapply(specifications, function(x) x$model_id, character(1)) ``` HTC, HyperFree and HyperSIGMA have explicit extraction implementations requiring exact reviewed source inventories, weights, preprocessing and rights. Their independent installed-package parity remains pending. HyperSIGMA's fixed fused features are distinct from its upstream trained downstream model. HyperVision, Dual-Stream-MHSI, SpectralEarth and SpectralGPT retain explicit execution blocks until their exact source/weights/input contracts are admitted. SpectralEarth is an adapted spectral track; SpectralGPT is a lossy multispectral auxiliary track. None is a qualified clinical model. Package-owned scratch reference IDs use `reference:` followed by `cnn1d`, `unet2d`, `cnn3d`, `spectral_vit`, `masked_autoencoder`, `contrastive_encoder`, `vae`, `conditional_gan`, `ddpm`, `multitask`, `domain_adversarial` or `distillation`. These have separate training objectives and synthetic gradient, restore and prediction tests. Distillation uses a scratch frozen teacher. Their worker protocol is distinct from the ordinary R engine workflow; registry visibility alone does not admit an R `hm_fit()` neural route. Reproduction is opt-in: resolve and review an exact runtime, use explicit training roles/group IDs, bound epochs, retain numeric artifacts and run adapter-specific parity. Do not run a model from a documentation build. GPU/Metal/ROCm/WSL, PEFT, quantization and scientific transfer are not qualified by CPU reference tests. See the runtime article before enabling any external execution.