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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

LexVec represents a cutting-edge word embedding technique that excels in various natural language processing applications by factorizing the Positive Pointwise Mutual Information (PPMI) matrix through the use of stochastic gradient descent. This methodology emphasizes greater penalties for mistakes involving frequent co-occurrences while also addressing negative co-occurrences. Users can access pre-trained vectors, which include a massive common crawl dataset featuring 58 billion tokens and 2 million words represented in 300 dimensions, as well as a dataset from English Wikipedia 2015 combined with NewsCrawl, comprising 7 billion tokens and 368,999 words in the same dimensionality. Evaluations indicate that LexVec either matches or surpasses the performance of other models, such as word2vec, particularly in word similarity and analogy assessments. The project's implementation is open-source, licensed under the MIT License, and can be found on GitHub, facilitating broader use and collaboration within the research community. Furthermore, the availability of these resources significantly contributes to advancing the field of natural language processing.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

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Screenshots View All

Integrations

AGAT Secure AI Platform Yes 
AICamp Yes 
Aerogram Yes 
Basalt Yes 
BlueFlame AI Yes 
Chatbot Arena Yes 
Cyte Yes 
DockClaw Yes 
Llama 4 Maverick Yes 
Magai Yes 
MindMac Yes 
Nebius Token Factory Yes 
Nutanix Enterprise AI Yes 
Ollama Yes 
Prefactor Yes 
Ragas Yes 
Revere Yes 
Trusys AI Yes 
fullmoon Yes 

Integrations

AGAT Secure AI Platform No 
AICamp No 
Aerogram No 
Basalt No 
BlueFlame AI No 
Chatbot Arena No 
Cyte No 
DockClaw No 
Llama 4 Maverick No 
Magai No 
MindMac No 
Nebius Token Factory No 
Nutanix Enterprise AI No 
Ollama No 
Prefactor No 
Ragas No 
Revere No 
Trusys AI No 
fullmoon 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 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 Yes 

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

Alexandre Salle

Country

Brazil

Website

github.com/alexandres/lexvec

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