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A Unified Library for Parameter-Efficient and Modular Transfer Learning
The main features of adapter-hub/adapters are: Computation and Optimization.
Open-source alternatives to adapter-hub/adapters include: bitsandbytes-foundation/bitsandbytes — bitsandbytes is a deep learning quantization tool and library designed to reduce the memory footprint of large… cupy/cupy — CuPy is a CUDA array computing library that implements a NumPy-compatible interface for executing array operations and… dask/dask — Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows… deap/deap. deepseek-ai/deepep — DeepEP is a distributed model accelerator and expert-parallel communication library designed to optimize the training… arogozhnikov/einops — Einops is a tensor manipulation library that provides a framework-agnostic interface for reshaping, Einstein…
bitsandbytes is a deep learning quantization tool and library designed to reduce the memory footprint of large language models. It serves as a GPU memory optimizer and quantization framework, compressing model weights and features to 8-bit and 4-bit precision to enable inference and training on hardware with limited memory. The project provides a framework for low-rank adaptation, allowing the fine-tuning of quantized models by combining 4-bit weights with small trainable matrices. It further distinguishes itself through memory paging, which moves optimizer states between CPU and GPU memory t
CuPy is a CUDA array computing library that implements a NumPy-compatible interface for executing array operations and numerical computing on NVIDIA GPUs. It serves as a GPU-accelerated numerical library and a CUDA-based SciPy implementation, offloading heavy calculations to graphics hardware to increase processing speed for scientific and engineering workloads. The library enables multi-framework tensor exchange, allowing data buffers to be shared between different deep learning frameworks using standardized memory layouts to avoid memory copies. It also supports custom GPU kernel integratio
Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows from single machines to large clusters. It functions as a cluster resource manager that orchestrates computational logic by representing tasks and their dependencies as directed acyclic graphs. This architecture allows the system to automate the distribution of workloads across available hardware while managing complex execution requirements. The project distinguishes itself through a lazy evaluation engine that defers data operations until they are explicitly requested, enabl
Einops is a tensor manipulation library that provides a framework-agnostic interface for reshaping, Einstein summation, and multi-dimensional array operations. It serves as an abstraction layer that works across NumPy, PyTorch, TensorFlow, and JAX, allowing for tensor transformations without changing the API. The library distinguishes itself through a declarative notation system that uses readable string patterns to describe tensor rearrangements and reductions. This approach includes an extended Einstein summation interface that supports multi-letter axis names and a named dimension mapping