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What is DBRX?

We are excited to introduce DBRX, a highly adaptable open LLM created by Databricks. This cutting-edge model sets a new standard for open LLMs by achieving remarkable performance across a wide range of established benchmarks. It offers both open-source developers and businesses the advanced features that were traditionally limited to proprietary model APIs; our assessments show that it surpasses GPT-3.5 and stands strong against Gemini 1.0 Pro. Furthermore, DBRX shines as a coding model, outperforming dedicated systems like CodeLLaMA-70B in various programming tasks, while also proving its capability as a general-purpose LLM. The exceptional quality of DBRX is further enhanced by notable improvements in training and inference efficiency. With its sophisticated fine-grained mixture-of-experts (MoE) architecture, DBRX pushes the efficiency of open models to unprecedented levels. In terms of inference speed, it can achieve performance that is twice as fast as LLaMA2-70B, and its total and active parameter counts are around 40% of those found in Grok-1, illustrating its compact structure without sacrificing performance. This unique blend of velocity and size positions DBRX as a transformative force in the realm of open AI models, promising to reshape expectations in the industry. As it continues to evolve, the potential applications for DBRX in various sectors are vast and exciting.

What is Code Llama?

Code Llama is a sophisticated language model engineered to produce code from text prompts, setting itself apart as a premier choice among publicly available models for coding applications. This groundbreaking model not only enhances productivity for seasoned developers but also supports newcomers in tackling the complexities of learning programming. Its adaptability allows Code Llama to serve as both an effective productivity tool and a pedagogical resource, enabling programmers to develop more efficient and well-documented software. Furthermore, users can generate code alongside natural language explanations by inputting either format, which contributes to its flexibility for various programming tasks. Offered for free for both research and commercial use, Code Llama is based on the Llama 2 architecture and is available in three specific versions: the core Code Llama model, Code Llama - Python designed exclusively for Python development, and Code Llama - Instruct, which is fine-tuned to understand and execute natural language commands accurately. As a result, Code Llama stands out not just for its technical capabilities but also for its accessibility and relevance to diverse coding scenarios.

Media

Media

Integrations Supported

1min.AI
BLACKBOX AI
CodeQwen
Cogent DataHub
Deep Infra
Double
GPT-3.5
IONOS Cloud AI Model Hub
Llama
Llama 2
Llama 3
Llama 3.1
Llama 3.2
Llama 3.3
Meta AI
Ollama
Pipeshift
Rayven
Tabby
Visplore

Integrations Supported

1min.AI
BLACKBOX AI
CodeQwen
Cogent DataHub
Deep Infra
Double
GPT-3.5
IONOS Cloud AI Model Hub
Llama
Llama 2
Llama 3
Llama 3.1
Llama 3.2
Llama 3.3
Meta AI
Ollama
Pipeshift
Rayven
Tabby
Visplore

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Free
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

Databricks

Company Location

United States

Company Website

www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

ai.meta.com/blog/code-llama-large-language-model-coding/

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