Distributed data parallel support for torch-rb

State-of-the-art transformers for Ruby

Data manipulation and transformation for audio signal processing

Deep learning recommendation systems for Ruby

Data loaders and abstractions for text and NLP

Computer vision datasets, transforms, and models for Ruby

A Ruby-native interface for fine-tuning machine learning models, starting with image classification using SigLIP2

Denoises audio (Speech Enhancement) using GTCRN model

Audio editing tool for podcasters, including denoising and removing non-speech period

A minimal, hackable LLM implementation with inference capabilities. Port of Karpathy's nanochat.

Train and run GPT models in Ruby using torch.rb. A minimal, educational implementation of GPT-2 style models.

N-Dimensional Array View for Torch::Tensors

Rumale::Torch provides the learning and inference by the neural network defined in torch.rb with the same interface as Rumale

Seq2seq transformer for transliteration in Ruby.

A seq2seq transformer suited for transliteration. Written in Ruby. Includes packages for training models

Media encoding and decoding for Torch.rb

Composable data loading for Ruby

DataParallel and distributed training utilities for torch.rb. Split batches across multiple GPUs automatically.

Self-Organized Map implementation using torch-rb

Training loop, LoRA, and optimization utilities for fine-tuning HuggingFace transformer models using torch-rb and transformers-rb. Supports full fine-tuning, LoRA adapters, learning rate scheduling, callbacks, and model serialization via safetensors.