llm-vscode is an extension for all things LLM. It uses llm-ls as its backend.

We also have extensions for:

• neovim
• jupyter

Previously huggingface-vscode.

This plugin supports "ghost-text" code completion, à la Copilot.

Requests for code generation are made via an HTTP request.

You can use the Hugging Face Inference API or your own HTTP endpoint, provided it adheres to the API specified here or here.

The list of officially supported models is located in the config template section.

The prompt sent to the model will always be sized to fit within the context window, with the number of tokens determined using tokenizers.

Hit Cmd+shift+a 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.

Install like any other vscode extension.

By default, this extension uses bigcode/starcoder & Hugging Face Inference API for the inference.

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: Llm: Login

If you previously logged in with huggingface-cli login on your system the extension will read the token from disk.

You can check the full list of configuration settings by opening your settings page (cmd+,) and typing Llm.

You can configure the endpoint to which requests will be sent.

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")

The request body will then 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 } };

const model = configuration.modelIdOrEndpoint;
let endpoint;
if (model.startswith("https://")) {
  endpoint = model;
} else {
  endpoint = `https://api-inference.huggingface.co/models/${model}`;
}

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

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

Note that the example above is a simplified version to explain what is happening under the hood.

By default, llm-ls is bundled with the extension. When developing locally or if you built your own binary because your platform is not supported, you can set the llm.lsp.binaryPath setting to the path of the binary.

llm-ls uses tokenizers to make sure the prompt fits the context_window.

To configure it, you have a few options:

• No tokenization, llm-ls will count the number of characters instead:

{
  "llm.tokenizer": null
}

• from a local file on your disk:

{
  "llm.tokenizer": {
    "path": "/path/to/my/tokenizer.json"
  }
}

• from a Hugging Face repository, llm-ls will attempt to download tokenizer.json at the root of the repository:

{
  "llm.tokenizer": {
    "repository": "myusername/myrepo"
  }
}

• from an HTTP endpoint, llm-ls will attempt to download a file via an HTTP GET request:

{
  "llm.tokenizer": {
    "url": "https://my-endpoint.example.com/mytokenizer.json",
    "to": "/download/path/of/mytokenizer.json"
  }
}

To test Code Llama 13B model:

1. Make sure you have the latest version of this extension.
2. Make sure you have supplied HF API token
3. Open Vscode Settings (cmd+,) & type: Llm: Config Template
4. From the dropdown menu, choose codellama/CodeLlama-13b-hf

Read more here about Code LLama.

To test Phind/Phind-CodeLlama-34B-v2 and/or WizardLM/WizardCoder-Python-34B-V1.0 :

1. Make sure you have the latest version of this extension.
2. Make sure you have supplied HF API token
3. Open Vscode Settings (cmd+,) & type: Llm: Config Template
4. From the dropdown menu, choose Phind/Phind-CodeLlama-34B-v2 or WizardLM/WizardCoder-Python-34B-V1.0

Read more about Phind-CodeLlama-34B-v2 here and WizardCoder-15B-V1.0 here.

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