turbopilot/README.md

# TurboPilot

TurboPilot is a self-hosted [copilot](https://github.com/features/copilot) clone which uses the library behind [llama.cpp](https://github.com/comex/llama.cpp) to run the [6 Billion Parameter Salesforce Codegen model](https://github.com/salesforce/CodeGen) in 4GiB of RAM. It is heavily based and inspired by on the [fauxpilot](https://github.com/fauxpilot/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](assets/screenrecording.gif)

## 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

#### Direct Download

You can download the pre-converted, pre-quantized models from [Google Drive](https://drive.google.com/drive/folders/1wFy1Y0pqoK23ZeMWWCp8evxWOJQVdaGh?usp=sharing). I've made the `multi` flavour models with 2B and 6B parameters available - these models are pre-trained on  `C`, `C++`, `Go`, `Java`, `JavaScript`, and `Python`

#### Convert The Models Yourself

Follow [this guide](https://github.com/ravenscroftj/turbopilot/wiki/Converting-and-Quantizing-The-Models) if you want to experiment with quantizing the models yourself.

### Running TurboPilot Server

Download the [latest binary](https://github.com/ravenscroftj/turbopilot/releases) 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](BUILD.md)

Run:

```bash
./codegen-serve -m ./models/codegen-6B-multi-ggml-4bit-quant.bin
```

The application should start a server on port `18080`

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:

```bash
./codegen-serve -t 6 -m ./models/codegen-6B-multi-ggml-4bit-quant.bin
```

### Using the API

#### Using the API with FauxPilot Plugin

To use the API from VSCode, I recommend the [vscode-fauxpilot](https://github.com/Venthe/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:

```json
{
    ... // 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:

```bash
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:

```json
{
 "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 **v0.0.1**:
- 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. Your mileage may vary on other operating systems. Please let me know if you try it elsewhere. I'm particularly interested in performance on Apple Silicon.
- 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 on suggestion length.
- Sometimes the server will run out of memory and crash. This is because it will try to use everything above your current location as context during generation. I'm working on a fix.

## Acknowledgements

- This project would not have been possible without [Georgi Gerganov's work on GGML and llama.cpp](https://github.com/ggerganov/ggml)
- It was completely inspired by [fauxpilot](https://github.com/fauxpilot/fauxpilot) which I did experiment with for a little while but wanted to try to make the models work without a GPU
- The frontend of the project is powered by [Venthe's vscode-fauxpilot plugin](https://github.com/Venthe/vscode-fauxpilot)
- The project uses the [Salesforce Codegen](https://github.com/salesforce/CodeGen) models.
- Thanks to [Moyix](https://huggingface.co/moyix) for his work on converting the Salesforce models to run in a GPT-J architecture. Not only does this [confer some speed benefits](https://gist.github.com/moyix/7896575befbe1b99162ccfec8d135566) but it also made it much easier for me to port the models to GGML using the [existing gpt-j example code](https://github.com/ggerganov/ggml/tree/master/examples/gpt-j)
- The model server uses [CrowCPP](https://crowcpp.org/master/) to serve suggestions.
- Check out the [original scientific paper](https://arxiv.org/pdf/2203.13474.pdf) for CodeGen for more info.