VSCode extension for testing open source code completion models

It was forked from tabnine-vscode & modified for making it compatible with open source code models on hf.co/models.

We also have neovim extension if you are a neovim user.

Installing

Install just like any other vscode extension.

By default, this extension is using bigcode/starcoder & Hugging Face Inference API for the inference. However, you can configure to make inference requests to your custom endpoint that is not Hugging Face Inference API. Thus, if you are using the default Hugging Face Inference AP inference, you'd need to provide HF API Token.

HF API token

You can supply your HF API token (hf.co/settings/token) with this command:

1. Cmd/Ctrl+Shift+P to open VSCode command palette
2. Type: Fabarta CodeX: Set API token

Testing

1. Create a new python file
2. Try typing def main():

Checking if the generated code is in The Stack

Hit Ctrl+Esc to check if the generated code is in in The Stack. This is a rapid first-pass attribution check using stack.dataportraits.org. We check for sequences of at least 50 characters that match a Bloom filter. This means false positives are possible and long enough surrounding context is necesssary (see the paper for details on n-gram striding and sequence length). The dedicated Stack search tool is a full dataset index and can be used for a complete second pass.

Developing

Make sure you've installed yarn on your system.

1. Clone this repo: git clone https://github.com/huggingface/huggingface-vscode
2. Install deps: cd huggingface-vscode && yarn install --frozen-lockfile
3. In vscode, open Run and Debug side bar & click Launch Extension

Checking output

You can see input to & output from the code generation API:

1. Open VSCode OUTPUT panel
2. Choose Fabarta CodeX

Configuring

You can configure: endpoint to where request will be sent and special tokens.

Example:

Let's say your current code is this:

import numpy as np
import scipy as sp
{YOUR_CURSOR_POSITION}
def hello_word():
    print("Hello world")

Then, the request body will look like:

const inputs = `{start token}import numpy as np\nimport scipy as sp\n{middle token}def hello_word():\n    print("Hello world"){end token}`
const data = {inputs, parameters:{max_new_tokens:256}};

const res = await fetch(endpoint, {
    body: JSON.stringify(data),
    headers,
    method: "POST"
});