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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.

Description

Introducing the next iteration of our open-source large language model, this version features model weights along with initial code for the pretrained and fine-tuned Llama language models, which span from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been developed using an impressive 2 trillion tokens and offer double the context length compared to their predecessor, Llama 1. Furthermore, the fine-tuned models have been enhanced through the analysis of over 1 million human annotations. Llama 2 demonstrates superior performance against various other open-source language models across multiple external benchmarks, excelling in areas such as reasoning, coding capabilities, proficiency, and knowledge assessments. For its training, Llama 2 utilized publicly accessible online data sources, while the fine-tuned variant, Llama-2-chat, incorporates publicly available instruction datasets along with the aforementioned extensive human annotations. Our initiative enjoys strong support from a diverse array of global stakeholders who are enthusiastic about our open approach to AI, including companies that have provided valuable early feedback and are eager to collaborate using Llama 2. The excitement surrounding Llama 2 signifies a pivotal shift in how AI can be developed and utilized collectively.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

AI-FLOW No 
AI/ML API No 
Batteries Included No 
BrandRank.AI No 
Chatterbox No 
Coginiti No 
Ema No 
Evertune No 
Featherless No 
Genaios No 
ModelOp No 
NVIDIA Brev No 
Ollama No 
PostgresML No 
Prompt Security No 
RankGPT No 
SectorFlow No 
Unify AI No 
Unsloth No 
ZenML No 

Integrations

AI-FLOW Yes 
AI/ML API Yes 
Batteries Included Yes 
BrandRank.AI Yes 
Chatterbox Yes 
Coginiti Yes 
Ema Yes 
Evertune Yes 
Featherless Yes 
Genaios Yes 
ModelOp Yes 
NVIDIA Brev Yes 
Ollama Yes 
PostgresML Yes 
Prompt Security Yes 
RankGPT Yes 
SectorFlow Yes 
Unify AI Yes 
Unsloth Yes 
ZenML Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

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 Yes 

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

Alexandre Salle

Country

Brazil

Website

github.com/alexandres/lexvec

Vendor Details

Company Name

Meta

Founded

2004

Country

United States

Website

ai.meta.com/llama/

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