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What is DeepSeek-V2?

DeepSeek-V2 represents an advanced Mixture-of-Experts (MoE) language model created by DeepSeek-AI, recognized for its economical training and superior inference efficiency. This model features a staggering 236 billion parameters, engaging only 21 billion for each token, and can manage a context length stretching up to 128K tokens. It employs sophisticated architectures like Multi-head Latent Attention (MLA) to enhance inference by reducing the Key-Value (KV) cache and utilizes DeepSeekMoE for cost-effective training through sparse computations. When compared to its earlier version, DeepSeek 67B, this model exhibits substantial advancements, boasting a 42.5% decrease in training costs, a 93.3% reduction in KV cache size, and a remarkable 5.76-fold increase in generation speed. With training based on an extensive dataset of 8.1 trillion tokens, DeepSeek-V2 showcases outstanding proficiency in language understanding, programming, and reasoning tasks, thereby establishing itself as a premier open-source model in the current landscape. Its groundbreaking methodology not only enhances performance but also sets unprecedented standards in the realm of artificial intelligence, inspiring future innovations in the field.

What is ALBERT?

ALBERT is a groundbreaking Transformer model that employs self-supervised learning and has been pretrained on a vast array of English text. Its automated mechanisms remove the necessity for manual data labeling, allowing the model to generate both inputs and labels straight from raw text. The training of ALBERT revolves around two main objectives. The first is Masked Language Modeling (MLM), which randomly masks 15% of the words in a sentence, prompting the model to predict the missing words. This approach stands in contrast to RNNs and autoregressive models like GPT, as it allows for the capture of bidirectional representations in sentences. The second objective, Sentence Ordering Prediction (SOP), aims to ascertain the proper order of two adjacent segments of text during the pretraining process. By implementing these strategies, ALBERT significantly improves its comprehension of linguistic context and structure. This innovative architecture positions ALBERT as a strong contender in the realm of natural language processing, pushing the boundaries of what language models can achieve.

Media

Media

Integrations Supported

SiliconFlow
Spark NLP

Integrations Supported

SiliconFlow
Spark NLP

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

DeepSeek

Date Founded

2023

Company Location

China

Company Website

deepseek.com

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

github.com/google-research/albert

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