LLM powered development for Neovim

Important

This is currently a work in progress, expect things to be broken!

llm.nvim is a plugin for all things LLM.

This project is influenced by copilot.vim and tabnine-nvim

Formerly hfcc.nvim.

Features

Code completion

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

Choose your model

Requests for code generation are made via curl to an HTTP endpoint.

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

Note

Requests via curl are a legacy feature. This plugin is evolving to become a front-end for llm-ls.

Starcoder

{
  model_eos = "<|endoftext|>",
  fim = {
    enabled = true,
    prefix = "<fim_prefix>",
    middle = "<fim_middle>",
    suffix = "<fim_suffix>",
  },
  model = "bigcode/starcoder",
  context_window = 8192,
}

Note

These are the default config values

CodeLlama

{
  model_eos = "<EOT>",
  fim = {
    enabled = true,
    prefix = "<PRE> ",
    middle = " <MID>",
    suffix = " <SUF>",
  },
  model = "codellama/CodeLlama-13b-hf",
  context_window = 4096,
}

Note

Spaces are important here

Install

With Inference API

1. Create and get your API token from here https://huggingface.co/settings/tokens.

2. Define how the plugin will read your token. For this you have multiple options, in order of precedence:

   1. Pass api_token = <your token> in plugin opts - this is not recommended if you use a versioning tool for your configuration files
   2. Set the LLM_NVIM_API_TOKEN environment variable
   3. You can define your HF_HOME environment variable and create a file containing your token at $HF_HOME/token
   4. Install the huggingface-cli and run huggingface-cli login - this will prompt you to enter your token and set it at the right path

3. Choose your model on the Hugging Face Hub, and, in order of precedence, you can either:

   1. Set the LLM_NVIM_MODEL environment variable
   2. Pass model = <model identifier> in plugin opts

With your own HTTP endpoint

All of the above still applies, but note:

• When api_token is set, it will be passed as a header: Authorization: Bearer <api_token>.

• Instead of setting a Hugging Face model identifier in model, set the URL for your HTTP endpoint.

Using packer

require("packer").startup(function(use)
  use {
    'huggingface/llm.nvim',
    config = function()
      require('llm').setup({
        -- cf Setup
      })
    end
  }
end)

Using lazy.nvim

require("lazy").setup({
  {
    'huggingface/llm.nvim',
    opts = {
      -- cf Setup
    }
  },
})

Using vim-plug

Plug 'huggingface/llm.nvim'

require('llm').setup({
  -- cf Setup
})

Setup

local llm = require('llm')

llm.setup({
  api_token = nil, -- cf Install paragraph
  model = "bigcode/starcoder", -- can be a model ID or an http(s) endpoint
  model_eos = "<|endoftext|>", -- needed to clean the model's output
  -- parameters that are added to the request body
  query_params = {
    max_new_tokens = 60,
    temperature = 0.2,
    top_p = 0.95,
    stop_tokens = nil,
  },
  -- set this if the model supports fill in the middle
  fim = {
    enabled = true,
    prefix = "<fim_prefix>",
    middle = "<fim_middle>",
    suffix = "<fim_suffix>",
  },
  debounce_ms = 150,
  accept_keymap = "<Tab>",
  dismiss_keymap = "<S-Tab>",
  max_context_after = 5000,
  max_context_before = 5000,
  tls_skip_verify_insecure = false,
  -- llm-ls integration
  lsp = {
    enabled = false,
    bin_path = vim.api.nvim_call_function("stdpath", { "data" }) .. "/llm_nvim/bin/llm-ls",
  },
  tokenizer_path = nil, -- when setting model as a URL, set this var
  context_window = 8192, -- max number of tokens for the context window
})

Commands

• LLMToggleAutoSuggest which enables/disables ghost text completion