Are you fed up of all of those so called "free" Copilot alternatives with paywalls and signups? Fear not my developer friend!

Twinny is the most no-nonsense locally hosted (or api hosted) AI code completion plugin for Visual Studio Code and any compatible editors (like VSCodium) designed to work seamlessly with:

• Ollama
• Ollama Web UI
• llama.cpp
• oobabooga/text-generation-webui
• LM Studio

Like Github Copilot but 100% free and private.

Install twinny on the
Visual Studio Code extension marketplace

Get AI based suggestions in real time. While coding you can let twinny autocomplete the code as you are typing.

Through the side bar, have a conversation with your model and get explanations about a function, ask it to write tests, ask for a refactor and much more.

• Single or multiline fill-in-middle completions
• Customisable prompt templates to add context to completions
• Easy installation via vscode extensions marketplace or by downloading and running a binary directly
• Customisable settings to change API provider, model name, port number and path
• Ollama, llamacpp, oobabooga and LM Studio API compatible
• Accept code solutions directly to editor
• Create new documents from code blocks
• Copy generated code solution blocks
• Chat history preserved per workspace

1. Install the VS code extension link (or if VSCodium)
2. Install ollama
3. Choose your model from the library (eg: codellama:7b)

ollama run codellama:7b

4. Open VS code (if already open a restart might be needed) and press ctr + shift + T to open the side panel.

You should see the 🤖 icon indicating that twinny is ready to use.

5. See Keyboard shortcuts to start using while coding 🎉

1. Install the VS code extension link (or if VSCodium)

2. Get llama.cpp / LM Studio / Oobabooga

3. Download and run the model locally using the chosen provider

4. Open VS code (if already open a restart might be needed) and press ctr + shift + T to open the side panel.

5. From the top ⚙️ icon open the settings page and in the Api Provider panel change from ollama to llamacpp (or others respectively).

6. In the left panel you should see the 🤖 icon indicating that twinny is ready to use.

7. See Keyboard shortcuts to start using while coding 🎉

Twinny supports the OpenAI API specification so in theory any provider should work as long as it supports the specification.

If you find that isn't the case please open an issue with details of how you are having problems.

When choosing an API provider the port and API path names will be updated automatically based on the provider you choose to use. These options can also be set manually.

The option for chat model name and fim model name are only applicable to Ollama and Oobabooga providers.

Twinny works with any model as long as it can run on your machine and it exposes a OpenAI API compliant endpoint.

Choosing a model is influenced a lot by the machine it will be running, a smaller model will give you a faster response but with a loss in accuracy.

There are two functionalities that twinny are expecting from a model:

Among LLM models, there are models called "instruct models", which are designed for a question & answer mode of chat.

All instruct models should work for chat generations, but the templates might need editing if using something other than codellama (they need to be updated with the special tokens).

• For computers with a good GPU, use: deepseek-coder:6.7b-base-q5_K_M (or any other good instruct model).

For FIM completions, you need to use LLM models called "base models". Unlike instruct models, base models will only try to complete your prompt. They are not designed to answer questions.

If using Llama the model must support the Llama special tokens.

• For computers with a good GPU, use: deepseek-coder:base or codellama-code (or any other good model that is optimised for code completions).
• For slower computers or computers using only CPU, use stable-code:3b-code-q4_0 (or any other small base model).

In the settings there is an option called useFileContext this will keep track of sessions, keystrokes, visits and recency of visited files in the current workspace. This can be enabled to help improve the quality of completions, it's turned off by default but I'm considering turning this on by default in the next release.

• If the server settings are incorrectly set chat and fim completion will not work, if this is the case please open an issue with your error message.
• Sometimes a restart of vscode is required for new settings to take effect, please open an issue if you are having problems with this.
• Using file context often causes unreliable completions for FIM because small models get confused when provided with more than one file context.
• See open issues on github to see any known issues that are not yet fixed.

If you have a problem with Twinny or have any suggestions please report them on github issues. Please include your vscode version and OS details in your issue.

We are actively looking for contributors who want to help improve the project, if you are interested in helping out please reach out on twitter.

Contributions are welcome please open an issue describing your changes and open a pull request when ready.

This project is under MIT licence, please read the LICENSE file for more information.

This plugin is provided "as is" and is under active development. This means that at times it may not work fully as expected.