README

FauxPilot

This is an attempt to build a locally hosted alternative to GitHub Copilot. It uses the SalesForce CodeGen models inside of NVIDIA's Triton Inference Server with the FasterTransformer backend.


  


 Prerequisites

You'll need:

 Docker
 docker compose = 1.28
 An NVIDIA GPU with Compute Capability = 6.0 and enough VRAM to run the model you want.
 nvidia-docker
 curl and zstd for downloading and unpacking the models.

Note that the VRAM requirements listed by setup.sh are total -- if you have multiple GPUs, you can split the model across them. So, if you have two NVIDIA RTX 3080 GPUs, you should be able to run the 6B model by putting half on each GPU.


 Support and Warranty

lmao

Okay, fine, we now have some minimal information on the wiki and a discussion forum where you can ask questions. Still no formal support or warranty though!



 Setup

This section describes how to install a Fauxpilot server and clients.

 Setting up a FauxPilot Server

Run the setup script to choose a model to use. This will download the model from Huggingface/Moyix in GPT-J format and then convert it for use with FasterTransformer.

Please refer to How to set-up a FauxPilot server.


 Client configuration for FauxPilot

We offer some ways to connect to FauxPilot Server. For example, you can create a client by how to open the Openai API, Copilot Plugin, REST API.

Please refer to How to set-up a client.


 Terminology
  API: Application Programming Interface
  CC: Compute Capability
  CUDA: Compute Unified Device Architecture
  FT: Faster Transformer
  JSON: JavaScript Object Notation 
  gRPC: Remote Procedure call by Google
  GPT-J: A transformer model trained using Ben Wang's Mesh Transformer JAX 
  REST: REpresentational State Transfer