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This project is a repository of resources for conversational artificial intelligence, providing infrastructure for the preparation, training, and evaluation of retrieval-based dialogue models. It offers a collection of large-scale dialogue datasets alongside a framework for cleaning, structuring, and serializing raw text into standardized formats suitable for machine learning workflows. The project distinguishes itself by providing a suite of tools for benchmarking model performance through automated scripts. It utilizes batch-based negative sampling to measure ranking accuracy and includes r
This repository provides supplementary material for our paper Constitutional AI: Harmlessness from AI Feedback.
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
A modular RL library to fine-tune language models to human preferences
Fine-Grained RLHF
The main features of allenai/finegrainedrlhf are: Conversational and Dialog Datasets, RLHF Frameworks.
Open-source alternatives to allenai/finegrainedrlhf include: polyai-ldn/conversational-datasets — This project is a repository of resources for conversational artificial intelligence, providing infrastructure for the… anthropics/constitutionalharmlessnesspaper — This repository provides supplementary material for our paper Constitutional AI: Harmlessness from AI Feedback. carperai/trlx — trlx is a reinforcement learning library and training framework designed to align large language models using human… cornell-rl/drpo. dunzeng/more. allenai/rl4lms — A modular RL library to fine-tune language models to human preferences.