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Description

Llama (Large Language Model Meta AI) stands as a cutting-edge foundational large language model aimed at helping researchers push the boundaries of their work within this area of artificial intelligence. By providing smaller yet highly effective models like Llama, the research community can benefit even if they lack extensive infrastructure, thus promoting greater accessibility in this dynamic and rapidly evolving domain. Creating smaller foundational models such as Llama is advantageous in the landscape of large language models, as it demands significantly reduced computational power and resources, facilitating the testing of innovative methods, confirming existing research, and investigating new applications. These foundational models leverage extensive unlabeled datasets, making them exceptionally suitable for fine-tuning across a range of tasks. We are offering Llama in multiple sizes (7B, 13B, 33B, and 65B parameters), accompanied by a detailed Llama model card that outlines our development process while adhering to our commitment to Responsible AI principles. By making these resources available, we aim to empower a broader segment of the research community to engage with and contribute to advancements in AI.

Description

Recent breakthroughs in natural language processing, comprehension, and generation have been greatly influenced by the development of large language models. This research presents a system that employs Ascend 910 AI processors and the MindSpore framework to train a language model exceeding one trillion parameters, specifically 1.085 trillion, referred to as PanGu-{\Sigma}. This model enhances the groundwork established by PanGu-{\alpha} by converting the conventional dense Transformer model into a sparse format through a method known as Random Routed Experts (RRE). Utilizing a substantial dataset of 329 billion tokens, the model was effectively trained using a strategy called Expert Computation and Storage Separation (ECSS), which resulted in a remarkable 6.3-fold improvement in training throughput through the use of heterogeneous computing. Through various experiments, it was found that PanGu-{\Sigma} achieves a new benchmark in zero-shot learning across multiple downstream tasks in Chinese NLP, showcasing its potential in advancing the field. This advancement signifies a major leap forward in the capabilities of language models, illustrating the impact of innovative training techniques and architectural modifications.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

No images available

Screenshots View All

No images available

Integrations

Agenta Yes 
AnythingLLM Yes 
Chatbot Arena Yes 
Cyte Yes 
Entry Point AI Yes 
Locally AI Yes 
Mastra AI Yes 
NVIDIA DGX Cloud Serverless Inference Yes 
NVIDIA Llama Nemotron Yes 
Nebius Token Factory Yes 
Ontosight.ai Yes 
Pinecone Rerank v0 Yes 
PostgresML Yes 
Prefactor Yes 
Singulr Yes 
Skott Yes 
Teradata VantageCloud Yes 
Void Editor Yes 
fullmoon Yes 
oMLX Yes 

Integrations

Agenta No 
AnythingLLM No 
Chatbot Arena No 
Cyte No 
Entry Point AI No 
Locally AI No 
Mastra AI No 
NVIDIA DGX Cloud Serverless Inference No 
NVIDIA Llama Nemotron No 
Nebius Token Factory No 
Ontosight.ai No 
Pinecone Rerank v0 No 
PostgresML No 
Prefactor No 
Singulr No 
Skott No 
Teradata VantageCloud No 
Void Editor No 
fullmoon No 
oMLX No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Meta

Founded

2004

Country

United States

Website

www.llama.com

Vendor Details

Company Name

Huawei

Founded

1987

Country

China

Website

huawei.com

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