Large language models (LLMs) have recently achieved human-level performance
on a range of professional and academic benchmarks. The accessibility of these
models has lagged behind their performance. State-of-the-art LLMs require
costly infrastructure; are only accessible via rate-limited, geo-locked, and
censored web interfaces; and lack publicly available code and technical
reports. In this paper, we tell the story of GPT4All, a popular open source
repository that aims to democratize access to LLMs. We outline the technical
details of the original GPT4All model family, as well as the evolution of the
GPT4All project from a single model into a fully fledged open source ecosystem.
It is our hope that this paper acts as both a technical overview of the
original GPT4All models as well as a case study on the subsequent growth of the
GPT4All open source ecosystem.

本研究论文通过介绍 GPT4All 技术细节和该项目的发展历程，旨在提供大规模语言模型的开放源代码和技术报告，以便实现 LLMs 的普及化和开放化。

GPT4All：一个开源压缩语言模型生态系统

GPT4All: An Ecosystem of Open Source Compressed Language Models

In this paper, we introduce TimeGPT, the first foundation model for time
series, capable of generating accurate predictions for diverse datasets not
seen during training. We evaluate our pre-trained model against established
statistical, machine learning, and deep learning methods, demonstrating that
TimeGPT zero-shot inference excels in performance, efficiency, and simplicity.
Our study provides compelling evidence that insights from other domains of
artificial intelligence can be effectively applied to time series analysis. We
conclude that large-scale time series models offer an exciting opportunity to
democratize access to precise predictions and reduce uncertainty by leveraging
the capabilities of contemporary advancements in deep learning.

TimeGPT 是时间序列的首个基础模型，能够生成准确的预测结果，我们通过评估其与统计学、机器学习和深度学习方法的性能对比发现，TimeGPT 在性能、效率和简洁性方面表现出色，研究结果显示了人工智能其他领域的洞见能够有效应用于时间序列分析，大规模时间序列模型为普遍获取精确预测和降低不确定性提供了激动人心的机会。