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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

No images available

Screenshots View All

Integrations

Amazon SageMaker Unified Studio Yes 
AnyAPI Yes 
BrandRank.AI Yes 
Cake AI Yes 
CompactifAI Yes 
Dedoctive Yes 
Diaflow Yes 
Fleak Yes 
Klee Yes 
Kong AI Gateway Yes 
LFM2.5 Yes 
Meta AI Yes 
Microsoft Foundry Agent Service Yes 
Nurix Yes 
RankLLM Yes 
Teneo.AI Yes 
Unsloth Yes 
Void Editor Yes 
kluster.ai Yes 

Integrations

Amazon SageMaker Unified Studio No 
AnyAPI No 
BrandRank.AI No 
Cake AI No 
CompactifAI No 
Dedoctive No 
Diaflow No 
Fleak No 
Klee No 
Kong AI Gateway No 
LFM2.5 No 
Meta AI No 
Microsoft Foundry Agent Service No 
Nurix No 
RankLLM No 
Teneo.AI No 
Unsloth No 
Void Editor No 
kluster.ai 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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