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A modular RL library to fine-tune language models to human preferences
The main features of allenai/rl4lms are: Model Training, Model Training and Fine-tuning, Reinforcement Learning Tools, RLHF Frameworks.
Projects with overlapping indexed features include: huggingface/trl — This library provides a comprehensive framework for fine-tuning, aligning, and distilling transformer-based language… huggingface/transformers — Transformers is a comprehensive library for machine learning that provides a unified interface for training,… carperai/trlx — trlx is a reinforcement learning library and training framework designed to align large language models using human… hiyouga/llama-factory — LLaMA-Factory is a comprehensive suite for dataset preparation, model fine-tuning, memory optimization, and… huggingface/peft — This library provides a framework for parameter-efficient fine-tuning, enabling the adaptation of large pretrained… lightning-ai/litgpt — LitGPT is a training and deployment framework for large language models, providing a suite of tools for pretraining,…
LLaMA-Factory is a comprehensive suite for dataset preparation, model fine-tuning, memory optimization, and standardized API deployment. It provides a unified platform for the supervised and reward-based fine-tuning of large language models and vision-language models. The framework includes a specialized toolkit for training vision-language models and a model serving interface that deploys trained models through high-performance APIs. It utilizes precision tuning and quantization techniques to reduce the hardware requirements and memory footprint of large models. The system covers data pipel
This library provides a framework for parameter-efficient fine-tuning, enabling the adaptation of large pretrained models by training only a small subset of parameters. It functions as a distributed model training system and optimization toolkit, designed to reduce the computational and memory requirements typically associated with full model fine-tuning. The project distinguishes itself through a suite of methods for modular adapter composition, including low-rank matrix decomposition and activation-based scaling. It supports the integration of multiple task-specific adapter modules, allowin
trlx is a reinforcement learning library and training framework designed to align large language models using human feedback. It serves as a distributed trainer and compute orchestrator for scaling high-parameter models across multiple GPUs and nodes. The project provides tools for reinforcement learning from human feedback and model alignment. It implements reward-model-based optimization and proximal policy optimization to refine model behavior based on goal-oriented rewards or human-labeled datasets. The framework covers distributed training strategies, including model parallelism, parame
Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and