Code Llama: What Is It & How to Use

Code Llama is a new AI system designed specifically for programming tasks like generating code and discussing code. Built by Meta, Code Llama leverages the scale and capabilities of large language models to provide advanced coding assistance.

In this blog post, we will look what is Code Llama and how to use Code Llama and Its performance.

What Is Code Llama

Code Llama is an AI model built on top of Llama 2 that generates and discusses code. It is free for research and commercial use. Code Llama is a large language model fine-tuned specifically for programming tasks. It uses text prompts to produce code snippets and engage in technical conversations.

Code Llama represents the state-of-the-art in publicly available AI systems for programming. It has potential as a productivity tool to help developers write better code faster. Code Llama could also lower barriers for new coders by assisting with training and answering questions. The model aims to make coding more efficient and accessible.

How Does Code Llama Work

Code Llama is an enhanced version of Llama 2, specialized for coding by training on additional programming data. It builds on Llama 2’s capabilities. Code Llama can generate code and discuss code using natural language or code prompts. It supports code completion, debugging, and other programming tasks across popular languages like Python and JavaScript.

Code Llama comes in three sizes – 7B, 13B, and 34B parameters. Each is trained on 500 billion tokens of code data. The smaller 7B and 13B models allow for faster serving and lower latency. The 34B model returns the best results for advanced coding assistance. Code Llama leverages scale and training to create more capable AI for programming.

How to Use Code Llama

Using Code Llama can seem overwhelming at first, but the process can be broken down into manageable steps. This guide will walk you through the process, from downloading the necessary files to running inference on the pre-trained models.

Step 1: Pre-requisites

Before you begin, ensure that you have wget and md5sum installed on your system. These tools are essential for downloading model files and ensuring their integrity.

sudo apt-get install wget coreutils

Step 2: Download Code Llama Model Weights

1. Visit the Meta AI Website: Navigate to Meta AI’s official website and find the section related to Code Llama. Accept their license agreement to proceed.
2. Receive the URL: After approval, you will get a signed URL via email.
3. Download Files: Open your terminal and run the download.sh script using the received URL.

bash download.sh

Note: Make sure you copy the URL correctly. If it starts with https://download.llamameta.net, you copied it correctly. Links expire after 24 hours or a certain number of downloads.

Step 3: Setup Your Environment

1. Install PyTorch/CUDA: Create a Conda environment and make sure PyTorch and CUDA are installed.
2. Clone the Repository: Download the Code Llama repository to your machine.
3. Install Dependencies: Navigate to the top-level directory of the cloned repository and run:

pip install -e .

Step 4: Choose Your Model and Set Model-Parallel Values

Code Llama offers different flavors and sizes of models. Depending on which one you choose, set the Model-Parallel (MP) value as follows:

• For 7B models, MP=1
• For 13B models, MP=2
• For 34B models, MP=4

Step 5: Run Inference

Now that you’re set up, you can use Code Llama for different tasks like code completion, code infilling, or instruction following.

For Code Completion

torchrun --nproc_per_node [MP value] example_code_completion.py \
    --ckpt_dir [Model Directory] \
    --tokenizer_path [Tokenizer Path] \
    --max_seq_len [Max Sequence Length] --max_batch_size [Batch Size]

For Code Infilling

torchrun --nproc_per_node [MP value] example_text_infilling.py \
    --ckpt_dir [Model Directory] \
    --tokenizer_path [Tokenizer Path] \
    --max_seq_len [Max Sequence Length] --max_batch_size [Batch Size]

For Instruction Following

torchrun --nproc_per_node [MP value] example_instructions.py \
    --ckpt_dir [Model Directory] \
    --tokenizer_path [Tokenizer Path] \
    --max_seq_len [Max Sequence Length] --max_batch_size [Batch Size]

And there you have it! You are now ready to unlock the vast potential of Code Llama for your code-related tasks.

Why Should We Use Code Llama

Code Llama can enhance programmer productivity on tasks like writing new code and debugging. It allows developers to focus on more creative aspects instead of repetitive work.

An open approach drives innovation and safety best with code models like Code Llama. Public availability lets the community fully evaluate capabilities, find vulnerabilities, and develop fixes. Open code models advance the field responsibly.

What is the Code Llama Performance

Code Llama was tested against benchmarks like HumanEval and MBPP, which measure code generation from docstrings or descriptions. Results showed Code Llama outperformed existing open source, code-specialized models. The 34B parameter Code Llama scored 53.7% on HumanEval and 56.2% on MBPP, surpassing other public solutions and on par with ChatGPT.

Responsible AI practices were critical. Extensive red team testing measured Code Llama’s risk of generating malicious code vs ChatGPT. Code Llama produced significantly safer responses to prompts attempting to elicit malicious code. Rigorous safety testing occurred before launch.

Conclusion

Code Llama represents an exciting advancement in AI capabilities for programming. This code-specialized model can generate code, complete code, and discuss code at state-of-the-art performance levels. Benchmark testing shows Code Llama surpassing previous open source models and rivaling proprietary solutions.

With responsible AI practices like rigorous safety testing and an open source approach, Code Llama has the potential to drive innovation in coding tools and workflows. The model lowers barriers for new programmers while enhancing productivity for experienced developers. As one of the most capable publicly available AI systems for programming tasks, Code Llama signifies impressive progress in code generation.

FAQs: Code Llama: What Is It & How to Use