ruda-nn 0.21.27

Ruda neural network layers, activation modules and losses.
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ruda-nn

Neural-network layers, activation modules, padding, and loss functions for Ruda models. Layers operate through tensor backend contracts rather than selecting a GPU driver themselves.

Interfaces

  • modules contains neural-network layer implementations and is re-exported at the crate root.
  • activation exposes activation modules; loss exposes loss functions.
  • Initializer is re-exported from ruda-model for parameter initialization.
  • LoRALinearConfig::init(base) freezes an existing dense projection and adds trainable A/B adapters. merge() produces a frozen, dropout-free dense projection.
  • loss::CausalLanguageModel separates decoder hidden states from the vocabulary head. CausalCrossEntropyConfig projects full-vocabulary token chunks, handles shifted/ignored labels, and returns an FP32 loss sum with the effective token count. Chunking does not recompute the backward graph.

Usage

Cargo package: ruda-nn. Rust import: ruda_nn.

[dependencies]
ruda-nn = "0.21"

Features

Default features: std, ruda-model/default.

Feature Purpose
std Enable standard-library model integration.
sparse Enable sparse layer support.
cuda Enable the CUDA tensor dependency.
fusion Enable device-fusion integration.

Links