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UltraChat is a collection of large-scale conversational datasets and instruction-tuning data designed for training and evaluating generative AI models. It provides structured JSON data consisting of complex, multi-round dialogue sequences intended to refine the performance of large language models in chat tasks. The project focuses on improving reasoning and response quality through a diverse set of interactions across multiple sectors. These datasets are used for supervised fine-tuning and instruction tuning workflows to improve how models follow complex directions and maintain context acros
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
Metaseq is a transformer sequence modeling toolkit designed for training, fine-tuning, and deploying sequence-to-sequence models using open pre-trained weights. It provides a comprehensive framework for large language model training, including dedicated tools for sequence dataset processing and a standalone inference server for generating text via API requests. The project features specialized utilities for model quantization to reduce parameter precision to eight bits, which lowers memory usage and increases inference speed. It also includes a checkpoint conversion pipeline to transform mode
Code and data associated with the AmbiEnt dataset in "We're Afraid Language Models Aren't Modeling Ambiguity" (Liu et al., 2023)
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"
The main features of orhonovich/unnatural-instructions are: Instruction Tuning, Instruction Tuning Datasets, Large Language Models.
Projects with overlapping indexed features include: yizhongw/self-instruct — Self-instruct is a framework for generating synthetic instruction datasets and fine-tuning large language models to… thunlp/ultrachat — UltraChat is a collection of large-scale conversational datasets and instruction-tuning data designed for training and… facebookresearch/metaseq — Metaseq is a transformer sequence modeling toolkit designed for training, fine-tuning, and deploying… alisawuffles/ambient — Code and data associated with the AmbiEnt dataset in "We're Afraid Language Models Aren't Modeling Ambiguity" (Liu et… artidoro/qlora — This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation… antimatter15/alpaca.cpp — alpaca.cpp is a high-performance local inference engine implemented in C++ for executing instruction-tuned large…