huggingface/peft

Features

• Parameter Efficient Fine-Tuning - Provides a library for parameter-efficient fine-tuning of large pretrained models.
• Large Language Model Fine-Tuning Frameworks - Provides a framework for adapting large pretrained models to downstream tasks through parameter-efficient fine-tuning.
• Large Language Model Optimization - Provides a toolkit for optimizing large language models via weight decomposition and activation scaling.
• Parameter Adaptation Techniques - Decomposes large model matrices into smaller low-rank matrices to enable fine-tuning with minimal trainable parameters.
• Distributed Training Frameworks - Functions as a distributed training framework designed for memory-efficient sharding and parameter offloading.
• Model Adapters - Enables modular adapter composition by merging and routing specialized adapter modules into base models.
• Low-Rank Decompositions - Reduces trainable parameter counts by representing large weight updates as the product of two significantly smaller matrices.
• Activation-Based Prompt Tuning - Implements activation-based tuning to adapt model behavior without modifying base weights.
• Distributed Training - Scales model training across multiple hardware devices using distributed parameter sharding.
• Distributed Training Sharding - Enables training of massive models by partitioning parameters and optimizer states across multiple hardware devices.
• Distributed Training Utilities - Splits model parameters and gradients across multiple devices to train massive models exceeding single-GPU memory.
• Weight Merging Utilities - Integrates trained adapter weights directly into the original model to eliminate inference latency.
• Memory Optimization Techniques - Implements memory offloading techniques to move model parameters and optimizer states to system memory during training.
• Weight Offloading - Moves sharded model parameters to system memory to reduce graphics memory consumption during training.
• Model Weight Reconstruction - Integrates trained adapter matrices directly into base model weights to create standalone models for efficient inference.
• Parameter-Efficient Adaptation - Integrates task-specific adapter modules into base architectures using parameter-efficient adaptation methods.
• Training Memory Management - Optimizes memory usage during training through parameter offloading and low-bit quantization.
• Frozen Base Models - Supports fine-tuning by keeping the primary model parameters frozen while training lightweight adapter layers.
• Activation Scaling Adapters - Trains learned vectors to rescale internal model activations for task-specific adaptation.
• Model Quantization - Supports low-bit quantization of model weights during fine-tuning to reduce memory footprint.
• Orthogonal Adaptation Strategies - Applies multiplicative orthogonal weight updates to frozen models to adapt them to new tasks.
• Application Development Tools - Library for parameter-efficient fine-tuning of large language models.
• Large Language Models - Library for parameter-efficient fine-tuning of models.
• Machine Learning - Library for adapting large pretrained models.
• Machine Learning Libraries - Parameter-efficient fine-tuning for large models.
• Model Fine Tuning - Implements state-of-the-art parameter-efficient fine-tuning techniques.
• Model Optimization and Deployment - Parameter-efficient methods for fine-tuning large pre-trained models.
• Model Training - Library for parameter-efficient fine-tuning of large models.
• Model Training and Fine-tuning - Parameter-efficient fine-tuning library.
• Computation and Optimization - Methods for parameter-efficient fine-tuning of pre-trained models.
• Large Language Models (LLMs) - Listed in the “Large Language Models (LLMs)” section of the The Incredible Pytorch awesome list.
• Weight Initialization - Uses gradient-based singular value decomposition to initialize adapter weights for faster convergence.
• Gradient-Based Initializers - Accelerates convergence by initializing adapter weights using gradient-based singular value decomposition.
• Adapter Routing Strategies - Routes and merges multiple trained low-rank modules to solve new tasks.
• Adapter Management - Combines several trained adapter modules using weighted averages to create unified adapter configurations.
• Adapter Dimension Suppressors - Identifies and suppresses specific adapter dimensions that cause catastrophic forgetting to recover original model knowledge.

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