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目录
Model catalogMT-NLG
MT

MT-NLG

Megatron-Turing Natural Language Generation model

Release date: 2022-01-28更新于: 2023-03-11 22:26:20.557679
Live demoGitHubHugging FaceCompare
Parameters
5400.0亿
Context length
2K
Chinese support
Not supported
Reasoning ability

Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology

MT-NLG

Model basics

Reasoning traces
Not supported
Thinking modes
Thinking modes not supported
Context length
2K tokens
Max output length
No data
Model type
基础大模型
Release date
2022-01-28
Model file size
No data
MoE architecture
No
Total params / Active params
5400.0B / N/A
Knowledge cutoff
No data
MT-NLG

Open source & experience

Code license
No data
Weights license
No data
GitHub repo
GitHub link unavailable
Hugging Face
Hugging Face link unavailable
Live demo
No live demo
MT-NLG

Official resources

Paper
Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model
DataLearnerAI blog
No blog post yet
MT-NLG

API details

API speed
No data
No public API pricing yet.
MT-NLG

Benchmark Results

No benchmark data to show.
MT-NLG

Publisher

Microsoft Azure
Microsoft Azure
View publisher details
Megatron-Turing Natural Language Generation model

Model Overview

MT-NLG是由NVIDIA和微软共同发表的一篇论文,介绍了他们使用DeepSpeed和Megatron来训练Megatron-Turing NLG 530B模型的过程和结果。

该论文中提到,他们通过对Megatron-Turing NLG模型进行调整和优化,以便在NVIDIA的GPU集群上进行分布式训练,使用了类似于数据并行的技术,从而能够将模型的规模扩大到530B个参数,成为当时世界上最大的生成式语言模型之一。

该论文还介绍了他们使用该模型来生成各种类型的文本,并展示了该模型在多项自然语言生成任务上的表现。这些结果表明,Megatron-Turing NLG 530B模型不仅能够生成高质量的文本,而且具有强大的可扩展性和性能,为未来自然语言处理技术的发展提供了重要的参考。

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