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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

RoBERTa enhances the language masking approach established by BERT, where the model is designed to predict segments of text that have been deliberately concealed within unannotated language samples. Developed using PyTorch, RoBERTa makes significant adjustments to BERT's key hyperparameters, such as eliminating the next-sentence prediction task and utilizing larger mini-batches along with elevated learning rates. These modifications enable RoBERTa to excel in the masked language modeling task more effectively than BERT, resulting in superior performance in various downstream applications. Furthermore, we examine the benefits of training RoBERTa on a substantially larger dataset over an extended duration compared to BERT, incorporating both existing unannotated NLP datasets and CC-News, a new collection sourced from publicly available news articles. This comprehensive approach allows for a more robust and nuanced understanding of language.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

No images available

Screenshots View All

Integrations

Aerogram Yes 
AiAssistWorks Yes 
Amazon SageMaker Unified Studio Yes 
Astera AI Agent Builder Yes 
Decopy AI Yes 
Dedoctive Yes 
Diaflow Yes 
Eldil AI Yes 
Firecrawl Yes 
Graydient AI Yes 
Haystack No 
Llama Guard Yes 
NVIDIA DGX Cloud Serverless Inference Yes 
NativeMind Yes 
Private Mind Yes 
PyMuPDF Yes 
Singulr Yes 
SpyderBot Yes 
Tiger Data Yes 
TypeThink Yes 

Integrations

Aerogram No 
AiAssistWorks No 
Amazon SageMaker Unified Studio No 
Astera AI Agent Builder No 
Decopy AI No 
Dedoctive No 
Diaflow No 
Eldil AI No 
Firecrawl No 
Graydient AI No 
Haystack Yes 
Llama Guard No 
NVIDIA DGX Cloud Serverless Inference No 
NativeMind No 
Private Mind No 
PyMuPDF No 
Singulr No 
SpyderBot No 
Tiger Data No 
TypeThink No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

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 No 
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

Meta

Founded

2004

Country

United States

Website

ai.facebook.com/blog/roberta-an-optimized-method-for-pretraining-self-supervised-nlp-systems/

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