20 open-source projects similar to xuefuzhao/instructionwild, 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.
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 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
The Largest-scale Chinese Medical QA Dataset: with 26,000,000 question answer pairs.
Alpaca dataset from Stanford, cleaned and curated
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"
We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. We welcome open-source enthusiasts to initiate any meaningful PR on this repo and integrate as many LLM related technologies as possible. 我们打造了方便研究人员上手和使用大模型等微调平台,我们欢迎开源爱好者发起任何有意义的pr!
Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback
This project provides a comprehensive Chinese language corpus designed to support the training and fine-tuning of large language models. It serves as a structured natural language processing resource, offering a collection of text data that includes dialogue, customer service interactions, and creative writing. The dataset is organized into distinct thematic categories, allowing for targeted model development across specific conversational and narrative contexts. By providing information in standardized, schema-agnostic text formats, the collection ensures portability across various machine l
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
Chinese medical dialogue data 中文医疗对话数据集
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
COLING 2022 CSL: A Large-scale Chinese Scientific Literature Dataset 中文科学文献数据集
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.
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