FINALLY! Open-Source "LLaMA Code" Coding Assistant (Tutorial)

Matthew Berman
30 Jan 202407:20

TLDRThe video introduces a groundbreaking local coding assistant called Cody, which operates independently of the internet, overcoming the limitations of traditional online-based assistants. The presenter guides viewers through the setup process using a local model powered by Olama, highlighting its speed and efficiency. Cody offers features like autocompletion, code generation, and context-aware editing, and is currently available for free. The video also showcases Cody's ability to understand and enhance existing code, making it a valuable tool for developers seeking a more integrated and efficient coding experience.

Takeaways

  • 🌐 Coding assistants have become essential for developers, but they typically require internet access and interaction with chat-based AI like GPT.
  • 🚀 A new solution allows for a local coding assistant that doesn't depend on internet connectivity, offering greater flexibility and reliability.
  • 🛠️ The local model is powered by Olama, which is open source and offers auto-completion capabilities for coding tasks.
  • 💻 The setup process involves using an extension for Visual Studio Code (VSCode) and downloading the Olama software.
  • 🔗 The video tutorial demonstrates how to install and configure the Cody AI extension in VSCode for local model usage.
  • 📋 The default model used by Cody is GPT-4, which is available for free with some rate limits, and the pro version is also free at the time of the video.
  • 📚 The local model used for inference is Cod llama 7B, which can be downloaded and set up to run locally.
  • 🔧 The video shows how to switch the Cody extension settings to use the local Olama model for auto-completion.
  • ✍️ Cody can generate code based on comments or specific requests, and it can also understand and work with existing code context.
  • 📝 Cody offers additional features like code documentation, code editing, explanation, and unit test generation, which are not dependent on local models.
  • 🌟 The video concludes by highlighting Cody's advantages over GitHub Copilot and encourages viewers to try it out.

Q & A

  • What is the main limitation of existing coding assistants like Chat GPT?

    -The main limitation is that they require an internet connection, which can be problematic for developers who are not always online or are in areas with limited connectivity, such as on a flight.

  • What is the solution presented in the video to overcome the internet connection limitation?

    -The solution is to use a local coding assistant called Cody, which is powered by a local model called CodeLLama and can run completely offline.

  • Is the local model used by Cody open source?

    -Yes, the local model used by Cody, CodeLLama, is open source.

  • Which version of CodeLLama is used in the video?

    -The video uses the 7 billion parameter version of CodeLLama.

  • What is the first step in setting up Cody on a MacBook Pro M2 Max?

    -The first step is to download and install Visual Studio Code (VSCode), which is the main coding editor used in the video.

  • How does one install the Cody extension on Visual Studio Code?

    -After opening Visual Studio Code, you go to the extensions button on the left side, search for 'Cody', and install the extension.

  • What is the default model used by Cody for autocompletion?

    -By default, Cody uses GPT-4 for autocompletion, which is completely free.

  • How does one switch Cody to use a local model for autocompletion?

    -In the Cody extension settings, you scroll down to the 'autocomplete Advanced provider' setting, select 'unstable olama', and configure it to use the local model through olama.

  • What is the size of the CodeLLama 7B model file?

    -The CodeLLama 7B model file is less than 4 GB in size.

  • How does Cody prove that it's running completely locally?

    -Cody proves it's running locally by showing the completion provider as 'unstable olama code llama' in the output and by the speed of code generation without needing to connect to the internet.

  • What are some additional features of Cody that are not powered by local models but are still useful?

    -Additional features include chatting with CLA 2.0, generating documentation in real-time, editing code with instructions, explaining code, looking for code smells, and generating unit tests.

Outlines

00:00

🌐 Introducing a Local Coding Assistant

The video discusses the limitations of existing online coding assistants that require internet access and introduces a local solution called Cody, which is an autocomplete coding assistant powered by a local model called Olama. The setup process for Cody is explained, including downloading Visual Studio Code, installing the Cody extension, and configuring it to use the local model. The video also mentions that Cody is sponsored and is currently free to use, with some rate limits for the free version and a pro version available for a small monthly fee.

05:01

🛠️ Utilizing Cody's Features and Local Model

The video demonstrates how to use Cody's features, including its ability to autocomplete code locally using the Olama model. It shows the process of downloading the model, configuring settings in Visual Studio Code, and using Cody to write and edit code. The video also highlights Cody's context understanding capabilities, its ability to generate code without typing, and its support for various Pro Models. Additionally, it showcases Cody's chat feature, code documentation, and unit test generation, emphasizing its versatility and potential advantages over GitHub Copilot.

Mindmap

Keywords

💡Coding Assistants

Coding assistants are AI-powered tools designed to aid developers in writing code more efficiently. They typically offer features like autocompletion, syntax highlighting, and error checking. In the video, the speaker discusses the limitations of current coding assistants that require internet access and introduces a local solution, which is a significant shift from the norm.

💡Local Model

A local model refers to a software model that runs on the user's own device, without the need for an internet connection to a remote server. This allows for faster response times and privacy, as the data stays on the local machine. The video introduces a local model for coding assistance, which is a departure from the cloud-based models that are common in the industry.

💡Olama

Olama is mentioned as the engine that powers the local model for the coding assistant. It is an open-source solution that allows for running AI models locally on the user's machine. This is significant because it provides the functionality of cloud-based AI without the need for constant internet access.

💡Visual Studio Code (VSCode)

Visual Studio Code is a popular open-source code editor developed by Microsoft. It supports a wide range of programming languages and has a large ecosystem of extensions that can enhance its functionality. In the context of the video, VSCode is the platform where the Codi extension is installed to provide coding assistance.

💡Codi

Codi is an AI-powered coding assistant that can be integrated with Visual Studio Code as an extension. It offers features like autocompletion, code generation, and context understanding. The video highlights the ability of Codi to work with local models, which is a unique feature that sets it apart from other coding assistants.

💡Autocompletion

Autocompletion is a feature in coding assistants that predicts and suggests the next part of the code a developer is likely to write, based on the current context and previous coding patterns. This can significantly speed up the coding process and reduce errors. The video showcases the autocompletion feature of Codi when working with a local model.

💡Code Llama 7B

Code Llama 7B is a specific version of the local model used by the Codi extension. The '7B' likely refers to the model having 7 billion parameters, which is a measure of the model's complexity and capacity for understanding and generating code. This model is used for local inference, which means it can provide coding assistance without needing to communicate with a remote server.

💡Inference

In the context of AI and machine learning, inference refers to the process of using a trained model to make predictions or generate outputs based on new input data. In the video, local inference is the process by which the Codi extension uses the local model to provide coding assistance without internet access.

💡Context Understanding

Context understanding in coding assistants means the ability to comprehend the existing code and its structure, and to provide relevant suggestions or completions based on that understanding. This feature is crucial for maintaining the integrity and consistency of the codebase. The video demonstrates Codi's ability to understand context and generate appropriate comments or edits.

💡Unit Tests

Unit tests are a method of testing individual components or units of a software application to determine if they are fit for purpose. They are an essential part of software development that helps ensure code quality and reliability. In the video, Codi is shown to have the capability to generate unit tests, which is a valuable feature for developers.

Highlights

Coding assistants have revolutionized the lives of developers worldwide.

Traditional coding assistants require an internet connection and interaction with chat GPT, which can be limiting.

A new solution allows for a local coding assistant, not dependent on internet access.

The local model is powered by Ola and is open source.

Cody is the sponsor of the video and will be used to demonstrate the setup.

Cody is available as an extension for Visual Studio Code (VS Code).

The model used is Code Llama, a 7 billion parameter version.

Cody offers autocompleting coding assistance that runs completely locally.

By default, Cody uses GPT-4 for assistance, which is free with some rate limits.

The pro version of Cody is currently free, with the next tier costing about $9 a month.

To set up local autocomplete, one needs to download and install Ola.

The model for inference is Code Llama 7B, which can be downloaded using Ola.

In the Cody settings, one can switch to an advanced provider for local autocomplete.

Cody can write code based on user prompts, such as generating a Fibonacci method.

Cody can also understand and add comments to existing code context.

Cody offers additional features like explaining code, finding code smells, and generating unit tests.

Cody is considered more powerful than GitHub Copilot due to its additional functionalities.

The video encourages viewers to try Cody and provides a link to the website.

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