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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.

What is Amazon CodeWhisperer?

Accelerate your app development process with a cutting-edge coding assistant powered by machine learning. This remarkable tool enhances the application building experience by delivering automated code suggestions that align with the existing code and comments in your integrated development environment (IDE). Developers can responsibly utilize artificial intelligence (AI) to produce applications that are not only syntactically accurate but also secure. Instead of searching for and tweaking code snippets from various sources, you can easily generate complete functions and logical blocks with just a few clicks. Stay immersed in your work without stepping outside the IDE, as you receive custom code suggestions in real-time for all your projects across languages like Java, Python, and JavaScript. Amazon CodeWhisperer is a machine learning-augmented service crafted to boost developer productivity through code recommendations informed by natural language comments and pre-existing code within the IDE. This innovative tool facilitates both frontend and backend development, saving valuable time by helping to generate code necessary for building and training your machine learning models, thereby simplifying the overall development workflow. With such advanced functionalities at their disposal, developers can push the boundaries of innovation more rapidly than ever before, transforming the way they approach coding challenges.

Media

Media

Integrations Supported

CodeQwen
Deep Infra
Llama
Llama 3
Llama 3.3
Ollama
Pipeshift

Integrations Supported

AWS Cloud9
Amazon EC2
Amazon Q Business
Amazon Web Services (AWS)
C
C#
C++
JavaScript
Kotlin
PHP
Ruby
Rust
SQL

API Availability

API Availability

Has API

Pricing Information

Free
Open source
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS
Windows
Mac
Linux

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

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

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/codewhisperer/

Categories and Features

AI Code Generators

Not specified

AI Code Refactoring

Not specified

AI Coding Assistants

Not specified

AI Coding Models

Not specified

AI Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Code Generators

Not specified

AI Code Refactoring

Not specified

AI Coding Assistants

Not specified

AI Copilots

Not specified

AI Tools

Not specified

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