Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

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

PanGu-α has been created using the MindSpore framework and utilizes a powerful setup of 2048 Ascend 910 AI processors for its training. The training process employs an advanced parallelism strategy that leverages MindSpore Auto-parallel, which integrates five different parallelism dimensions—data parallelism, operation-level model parallelism, pipeline model parallelism, optimizer model parallelism, and rematerialization—to effectively distribute tasks across the 2048 processors. To improve the model's generalization, we gathered 1.1TB of high-quality Chinese language data from diverse fields for pretraining. We conduct extensive tests on PanGu-α's generation capabilities across multiple situations, such as text summarization, question answering, and dialogue generation. Additionally, we examine how varying model scales influence few-shot performance across a wide array of Chinese NLP tasks. The results from our experiments highlight the exceptional performance of PanGu-α, demonstrating its strengths in handling numerous tasks even in few-shot or zero-shot contexts, thus showcasing its versatility and robustness. This comprehensive evaluation reinforces the potential applications of PanGu-α in real-world scenarios.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

No images available

Screenshots View All

No images available

Integrations

AGAT Secure AI Platform Yes 
Athina AI Yes 
Atomic Chat Yes 
Azure Marketplace Yes 
BlueFlame AI Yes 
CoSpaceGPT Yes 
Evertune Yes 
Jan Yes 
Jitterbit Yes 
Kong AI Gateway Yes 
Mastra AI Yes 
Ontosight.ai Yes 
Overseer AI Yes 
PostgresML Yes 
PyMuPDF Yes 
Snack Prompt Yes 
TypeThink Yes 
Undrstnd Yes 
Unframe Yes 

Integrations

AGAT Secure AI Platform No 
Athina AI No 
Atomic Chat No 
Azure Marketplace No 
BlueFlame AI No 
CoSpaceGPT No 
Evertune No 
Jan No 
Jitterbit No 
Kong AI Gateway No 
Mastra AI No 
Ontosight.ai No 
Overseer AI No 
PostgresML No 
PyMuPDF No 
Snack Prompt No 
TypeThink No 
Undrstnd No 
Unframe 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 Yes 
Mac Yes 
Linux Yes 
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

arxiv.org/abs/2104.12369

Product Features

Alternatives

Alternatives

OPT Reviews

OPT

Meta
Gemini Reviews

Gemini

Google
PanGu-Σ Reviews

PanGu-Σ

Huawei
Alpaca Reviews

Alpaca

Stanford Center for Research on Foundation Models (CRFM)