Projects sharing features with Alpaca CoT

Dromedary: towards helpful, ethical and reliable LLMs.

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

LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions

WizardLM is a large language model and instruction-tuning framework designed to execute sophisticated coding, mathematical, and conversational tasks. It functions as an AI system for mathematical reasoning and code generation, as well as a synthetic dataset generator used to train other language models. The project is distinguished by its evolutionary instruction tuning, which uses a method to rewrite simple instructions into complex tasks. This process expands training dataset difficulty and produces a high volume of open-domain tasks across various difficulty levels. The system covers capa

This is the repository for the project Symbolic Knowledge Distillation: from General Language Models to Commonsense Models

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

EMNLP 2024 中文领域心理健康对话大模型MeChat

This is the repo for the Code Alpaca project, which aims to build and share an instruction-following LLaMA model for code generation. This repo is fully based on Stanford Alpaca ,and only changes the data used for training. Training approach is the same.

This is the repository for the EMNLP 2023 paper PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation.

Chinese medical dialogue data 中文医疗对话数据集

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.

COLING 2022 CSL: A Large-scale Chinese Scientific Literature Dataset 中文科学文献数据集

\Accepted by TBD\ Code for: AugGPT: Leveraging ChatGPT for Text Data Augmentation

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