On August 24th, Meta AI officially announced “Code Llama.” Code Llama is an AI coding solution based on the Llama2 large language model (LLM) and made available to the community under a licensing agreement. This tool constitutes a refined adaptation of Llama2, trained to excel in computer code generation and discourse. Code Llama has multiple models. One of the models meets diverse coding needs across various programming languages, like Python, Java, Typescript, C++, PHP, C#, etc. Other models are known as Code Llama Python & Code Llama Instruct.
Code Llama Python & Code Llama Instruct
Code Llama Python works well for Python-centric applications. These two models focus on understanding towards understanding, elucidating, and engaging in discussions related to code.
Code Llama Instruct represents a refined version of Code Llama, recommended by Meta for the actual code generation. Moreover, these models are available in different parameter sizes, catering to various operational scenarios. Code Llama has parameter configurations of 7 billion, 14 billion, and 34 billion.
Meta indicated that the 7B parameter models can function effectively on a single GPU. In contrast, the 14B and 34B parameter models require more robust hardware. They can handle more complex tasks.
Code Llama can be widely accessible under the same community license agreement that applies to Llama2. This implies that one can use it for personal or business purposes, with the stipulation of giving appropriate credit. This development holds immense promise for businesses and individuals demanding extensive language models in coding scenarios. Industries such as fintech, which have historically faced limitations in support from AI and major tech companies, stand to gain significantly from this advancement.
The emergence of a free-to-use, community-licensed alternative anchored in Meta’s highly acclaimed Llama2 large language model has the potential to create a more equitable landscape for blockchain and crypto ventures characterized by modest development teams.
The featured image is from Google
