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

We also have extensions for:

• neovim
• jupyter

Currently supported model is StartCoder from BigCode project. Find more info here.

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.

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: Hugging Face Code: Set API token

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

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.

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

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

1. Open VSCode OUTPUT panel
2. Choose Hugging Face Code

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_world():
    print("Hello world")

Then, the request body will look like:

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

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

const json = await res.json() as any as {generated_text: string};  // {"generated_text": ""}