Introducing Code Llama, an AI Tool for Coding

Generative AI applications have taken the world by storm. OpenAI, with its Chat GPT 3.5 and ChatGPT 4.0, and Google, with its OpenAI, have a head start. Code Llama is not just Meta’s response to such applications but also a game-changer in the generative AI space.

What is Code Llama?

Code Llama is essentially an AI-powered coding tool. It is Meta’s large language model (LLM) built on Meta’s Llama2 language model. The model offers advanced coding capabilities and supports most languages, including Python, C++, C#, Bash, PHP, Java, and Typescript (Javascript).

Users may use Code Llama to

Generate code and automate coding tasks. Users may describe a function in natural, conversational language, and Code Llama generates the code. For instance, if a user writes “Write a function that reverses a string” in the text prompt, it instantly generates the code.
Debug and improve code quality. Users may input code and ask the model to refine it. It becomes especially handy for competing and debugging complex code.
Translate code from one language to another. For instance, if users input a code in Python, it will generate the equivalent Java code.
Improve documentation associated with the code. Normally, users describe the desired functionality, and the LLM generates a code to enable the same. It works the other way around as well. If users input a piece of code, it writes a natural language description of what the code does.

Applying it makes workflows faster and speeds up coding. Overall process efficiency improves, with cost and effort reducing in a big way.

The underlying LLM

LLMs are artificial intelligence models built using deep learning techniques. Unlike conventional AI applications, these models understand and generate natural human-like text. LLMs comprehend context, answer questions, translate languages, and perform other language-related tasks.

Popular LLMs today include

OpenAI’s GPT-3 (Generative Pre-trained Transformer 3) and GPT-4,
Google’s LaMDA with its interface Bard
BERT (Bidirectional Encoder Representations from Transformers)
T5 (Text-to-Text Transfer Transformer).
Meta’s Llama 2

Until now, Llama 2 suffered a major drawback compared to other LLMs in that it didn’t have an easy-to-use application interface. OpenAI has ChatGPT, and Google’s LaMDA has Bard. It overcomes Bard’s drawback to a large extent.

How does Code Lama work?

All LLMs work almost the same way, some technical differences notwithstanding. When a user provides the LLM input prompts, the model triggers its neural network to predict the most plausible follow-on content.

A typical LLM has billions of variables or “parameters” modelled after the human brain. The natural network model:

Tracks words in a sequence to establish context.
Capture intricate language patterns to generate coherent and relevant text.
Apply mathematical techniques to trace the influence and dependability of distant data elements.

The difference between different LLMs, such as, say, Llama 2 and GPT 4.0, is the different weights assigned to parameters. These variances alter the human-like responses given by the different LLMs.

Pretraining and fine-tuning refine these models further. LLM models come pre-trained on a vast corpus of data sourced from the Internet. Users fine-tune LLM models for answering questions, translations, sentiment analysis, or other tasks.

Meta has trained Llama 2’s neural network with two trillion pus tokens from sources such as

Common Crawl, an archive of billions of web pages.
Wikipedia
Public domain books from Project Gutenberg.

Meta created it by training Llama 2 on code-specific datasets and sampling more data from the same dataset for longer. The developers have also optimised the model responses through reinforcement learning with human feedback (RLHF).

The variants

Meta has released Code Llama in three models. Each model has been trained with 500B code tokens and code-related data. These models differ in their capability to address different serving and latency requirements.

The three models have a parameter size of 7B, 13B and 34B parameters, respectively. A larger parameter size indicates training with larger data, meaning more reliable responses. But the smaller 7B and 13B models are faster and more suitable for low latency tasks such as real-time code completion. The 7B and 13B base and instruct models also have fill-in-the-middle (FIM) capability, enabling them to insert code into existing code. Such capabilities give Code Llama advanced functionality, such as code completion, out of the box.

There are also additional variations of Code Llama: Code Llama – Python and Code Llama – Instruct.

Code Llama – Python is fine-tuned on 100B tokens of Python code, making it a language-specialised version of it. Python is the most benchmarked language for code generation. PyTorch is a very popular open-source machine learning framework. The variant provides additional utility to use on this stack.

Code Llama – Instruct continues the training process. Users feed the model with natural language instruction input, and also the expected output. Such insights make the model better at delivering relevant and accurate responses.

How is Code Llama different from other LLMs?

Llama2 differs from other popular LLMs such as GPT-3, GPT-4, PaLM and PaLM2 in that it permits commercial use of the underlying code.

Most LLMs, including OpenAI and LaMDA, are close-sourced, with users having no control over what goes on inside the model. All they can do is fine-tune the model using APIs. Llama 2 allows users to download the model and modify the code. The only notable restriction is companies with 700+ million monthly users needing permission from Meta to use Llama. This makes the code off-limits to Apple, Google, Amazon and other biggies. But any small and medium enterprise can use Llama to develop AI-powered applications.

The Llama 2 licence allows the community to “use, reproduce, distribute, copy, create derivative works of, and make modifications” to the Llama Materials published by Meta. This allows enterprises to develop applications with greater control over the underlying workings.

The impact of Code Llama

Code Llama supports software engineers in all spheres, including normal commercial coding. Industry use-cases, research, open source projects, NGOs and other applications. Enterprises have already started to co-opt Code Llama in their coding projects to write more robust and high-quality code.

As with most generative AI tools, Code Llama will not replace human coders completely, But it can disrupt the programming workplace in a big way. Code Llama

Lowers the entry barrier for people learning to code.
Improves the productivity and efficiency of existing programmers. Enterprises can get more work done with their existing workforce.
Improves the quality of work of existing programmers. Code Llama take over predictable and repeatable tasks. Developers can focus on the most human-centric aspects of their job.

Code Llama is still under development. All indicators point to a powerful tool that will take software development to new levels of productivity and efficiency.

With Meta-making Code Llama free and publically available, the tech community can identify any issues and fix vulnerabilities promptly. And enterprising users can create new innovative tools based on Code Llama. Most big developments in computing have been on top of open research and experimentation, and Code Llama promises to enable such significant breakthroughs.

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