12 open-source projects similar to gururise/alpacadatacleaned, ranked by shared indexed features. Tags may describe platforms or build tools rather than the same primary purpose. Check each project’s use case, license, and deployment requirements before treating it as a replacement.
ChatAlpaca is a chat dataset that aims to help researchers develop models for instruction-following in multi-turn conversations. The dataset is an extension of the Stanford Alpaca data, which contains multi-turn instructions and their corresponding responses.
This project is an instruction tuning framework and synthetic data generator that uses high-capacity teacher models to produce instruction-following pairs for training smaller student models. It provides datasets and tools for supervised instruction tuning and reinforcement learning from human feedback. The framework specializes in cross-lingual tuning, offering high-quality instruction-following examples in English and Chinese to improve model generalization across different scripts. It includes a reward modeling tool for creating preference datasets and comparative ratings used to train rew
BELLE is a specialized implementation of Chinese conversational large language models, encompassing a full instruction tuning framework. It provides a pipeline for training, evaluating, and deploying models optimized for natural language understanding and dialogue tasks in the Chinese language. The project is distinguished by its integrated approach to model refinement, combining the curation of multi-million entry instruction datasets with a distributed training pipeline. This pipeline supports both full fine-tuning and low-rank adaptation to optimize conversational performance. The system
This repository contains the Unnatural Instructions dataset. Unnatural Instructions is a dataset of instructions automatically generated by a Large Language model. See full details in the paper: "Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor"
Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback
Let ChatGPT teach your own chatbot in hours with a single GPU!
A collection of modular datasets generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer
TigerBot: A multi-language multi-task LLM
Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation (EMNLP 2023) | Paper | Project Website | 🤗 Data | 🤗 Model | Da Yin , Xiao Liu , Fan Yin , Ming Zhong , Hritik Bansal , Jiawei Han , Kai-Wei Chang
We release InstructWild v2 under data v2 dir, which includes over 110K high-quailty user-based instructions. We did not use self-instruct to generate any instructions. We also label a subset of these instructions with instruction type and speical tag. Please see README for details.
Self-instruct is a framework for generating synthetic instruction datasets and fine-tuning large language models to improve their instruction-following capabilities. It provides a pipeline for aligning pretrained models with human intentions through a supervised fine-tuning workflow. The system utilizes a synthetic data generator that uses a seed set of tasks to prompt a model to create new instructional data. It includes an instruction dataset curator to remove redundant or low-quality entries, maintaining dataset diversity through a filtered task pool. The framework covers the full alignme