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TurboPilot 🚀

Mastodon Follow BSD Licensed Time Spent

TurboPilot is a self-hosted copilot clone which uses the library behind llama.cpp to run the 6 Billion Parameter Salesforce Codegen model in 4GiB of RAM. It is heavily based and inspired by on the fauxpilot project.

NB: This is a proof of concept right now rather than a stable tool. Autocompletion is quite slow in this version of the project. Feel free to play with it, but your mileage may vary.

a screen recording of turbopilot running through fauxpilot plugin

✨ Now Supports StableCode 3B Instruct simply use TheBloke's Quantized GGML models and set -m stablecode.

✨ New: Refactored + Simplified: The source code has been improved to make it easier to extend and add new models to Turbopilot. The system now supports multiple flavours of model

✨ New: Wizardcoder, Starcoder, Santacoder support - Turbopilot now supports state of the art local code completion models which provide more programming languages and "fill in the middle" support.

🤝 Contributing

PRs to this project and the corresponding GGML fork are very welcome.

Make a fork, make your changes and then open a PR.

👋 Getting Started

The easiest way to try the project out is to grab the pre-processed models and then run the server in docker.

Getting The Models

You have 2 options for getting the model

Option A: Direct Download - Easy, Quickstart

You can download the pre-converted, pre-quantized models from Huggingface.

For low RAM users (4-8 GiB), I recommend StableCode and for high power users (16+ GiB RAM, discrete GPU or apple silicon) I recomnmend WizardCoder.

Turbopilot still supports the first generation codegen models from v0.0.5 and earlier builds. Although old models do need to be requantized.

You can find a full catalogue of models in MODELS.md.

Option B: Convert The Models Yourself - Hard, More Flexible

Follow this guide if you want to experiment with quantizing the models yourself.

⚙️ Running TurboPilot Server

Download the latest binary and extract it to the root project folder. If a binary is not provided for your OS or you'd prefer to build it yourself follow the build instructions

Run:

./turbopilot -m starcoder -f ./models/santacoder-q4_0.bin

The application should start a server on port 18080, you can change this with the -p option but this is the default port that vscode-fauxpilot tries to connect to so you probably want to leave this alone unless you are sure you know what you're doing.

If you have a multi-core system you can control how many CPUs are used with the -t option - for example, on my AMD Ryzen 5000 which has 6 cores/12 threads I use:

./codegen-serve -t 6 -m starcoder -f ./models/santacoder-q4_0.bin

To run the legacy codegen models. Just change the model type flag -m to codegen instead.

NOTE: the latest version of GGML requires that you re-quantize your codegen models. Old models downloaded from here will no longer work. I am working on providing updated quantized codegen models

📦 Running From Docker

You can also run Turbopilot from the pre-built docker image supplied here

You will still need to download the models separately, then you can run:

docker run --rm -it \
  -v ./models:/models \
  -e THREADS=6 \
  -e MODEL_TYPE=starcoder \
  -e MODEL="/models/santacoder-q4_0.bin" \
  -p 18080:18080 \
  ghcr.io/ravenscroftj/turbopilot:latest

Docker and CUDA

As of release v0.0.5 turbocode now supports CUDA inference. In order to run the cuda-enabled container you will need to have nvidia-docker enabled, use the cuda tagged versions and pass in --gpus=all to docker with access to your GPU like so:

docker run --gpus=all --rm -it \
  -v ./models:/models \
  -e THREADS=6 \
  -e MODEL="/models/codegen-2B-multi-ggml-4bit-quant.bin" \
  -p 18080:18080 \
  ghcr.io/ravenscroftj/turbopilot:v0.1.0-cuda11

Swap ghcr.io/ravenscroftj/turbopilot:v0.1.0-cuda11 for ghcr.io/ravenscroftj/turbopilot:v0.1.0-cuda12 if you are using CUDA 12 or later.

You will need CUDA 11 or CUDA 12 later to run this container. You should be able to see /app/turbopilot listed when you run nvidia-smi.

Executable and CUDA

As of v0.0.5 a CUDA version of the linux executable is available - it requires that libcublas 11 be installed on the machine - I might build ubuntu debs at some point but for now running in docker may be more convenient if you want to use a CUDA GPU.

🌐 Using the API

Support for the official Copilot Plugin

Support for the official VS Code copilot plugin is underway (See ticket #11). The API should now be broadly compatible with OpenAI.

Using the API with FauxPilot Plugin

To use the API from VSCode, I recommend the vscode-fauxpilot plugin. Once you install it, you will need to change a few settings in your settings.json file.

  • Open settings (CTRL/CMD + SHIFT + P) and select Preferences: Open User Settings (JSON)
  • Add the following values:
{
    ... // other settings

    "fauxpilot.enabled": true,
    "fauxpilot.server": "http://localhost:18080/v1/engines",
}

Now you can enable fauxpilot with CTRL + SHIFT + P and select Enable Fauxpilot

The plugin will send API calls to the running codegen-serve process when you make a keystroke. It will then wait for each request to complete before sending further requests.

Calling the API Directly

You can make requests to http://localhost:18080/v1/engines/codegen/completions which will behave just like the same Copilot endpoint.

For example:

curl --request POST \
  --url http://localhost:18080/v1/engines/codegen/completions \
  --header 'Content-Type: application/json' \
  --data '{
 "model": "codegen",
 "prompt": "def main():",
 "max_tokens": 100
}'

Should get you something like this:

{
 "choices": [
  {
   "logprobs": null,
   "index": 0,
   "finish_reason": "length",
   "text": "\n  \"\"\"Main entry point for this script.\"\"\"\n  logging.getLogger().setLevel(logging.INFO)\n  logging.basicConfig(format=('%(levelname)s: %(message)s'))\n\n  parser = argparse.ArgumentParser(\n      description=__doc__,\n      formatter_class=argparse.RawDescriptionHelpFormatter,\n      epilog=__doc__)\n  "
  }
 ],
 "created": 1681113078,
 "usage": {
  "total_tokens": 105,
  "prompt_tokens": 3,
  "completion_tokens": 102
 },
 "object": "text_completion",
 "model": "codegen",
 "id": "01d7a11b-f87c-4261-8c03-8c78cbe4b067"
}

👉 Known Limitations

Again I want to set expectations around this being a proof-of-concept project. With that in mind. Here are some current known limitations.

As of v0.0.2:

  • The models can be quite slow - especially the 6B ones. It can take ~30-40s to make suggestions across 4 CPU cores.
  • I've only tested the system on Ubuntu 22.04 but I am now supplying ARM docker images and soon I'll be providing ARM binary releases.
  • Sometimes suggestions get truncated in nonsensical places - e.g. part way through a variable name or string name. This is due to a hard limit of 2048 on the context length (prompt + suggestion).

👏 Acknowledgements