llm.nvim is a plugin for all things LLM. It uses llm-ls as a backend.

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

Formerly hfcc.nvim.

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 APIs listed in backend.

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

llm.nvim can interface with multiple backends hosting models.

You can override the url of the backend with the LLM_NVIM_URL environment variable. If url is nil, it will default to the Inference API's default url

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

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_HF_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

Note: the model's value will be appended to the url like so : {url}/{model} as this is how we route requests to the right model.

API

Refer to Ollama's documentation on how to run ollama. Here is an example configuration:

{
  model = "codellama:7b",
  url = "http://localhost:11434/api/generate",
  -- cf https://github.com/ollama/ollama/blob/main/docs/api.md#parameters
  request_body = {
    -- Modelfile options for the model you use
    options = {
      temperature = 0.2,
      top_p = 0.95,
    }
  }
}

Note: model's value will be added to the request body.

Refer to Ollama's documentation on how to run ollama. Here is an example configuration:

{
  model = "codellama",
  url = "http://localhost:8000/v1/completions",
  -- cf https://github.com/abetlen/llama-cpp-python?tab=readme-ov-file#openai-compatible-web-server
  request_body = {}
}

Note: model's value will be added to the request body.

API

Refer to TGI's documentation on how to run TGI. Here is an example configuration:

{
  model = "bigcode/starcoder",
  url = "http://localhost:8080/generate",
  -- cf https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/generate
  request_body = {
    parameters = {
      temperature = 0.2,
      top_p = 0.95,
    }
  }
}

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

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

By default, llm-ls is installed by llm.nvim the first time it is loaded. The binary is downloaded from the release page and stored in:

vim.api.nvim_call_function("stdpath", { "data" }) .. "/llm_nvim/bin"

When developing locally, when using mason or if you built your own binary because your platform is not supported, you can set the lsp.bin_path setting to the path of the binary. You can also start llm-ls via tcp using the --port [PORT] option, which is useful when using a debugger.

lsp.version is used only when llm.nvim downloads llm-ls from the release page.

You can install llm-ls via mason.nvim. To do so, run the following command:

:MasonInstall llm-ls

Then reference llm-ls's path in your configuration:

{
  -- ...
  lsp = {
    bin_path = vim.api.nvim_call_function("stdpath", { "data" }) .. "/mason/bin/llm-ls",
  },
  -- ...
}

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:

{
  tokenizer = nil,
}

• from a local file on your disk:

{
  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:

{
  tokenizer = {
    repository = "myusername/myrepo"
    api_token = nil -- optional, in case the API token used for the backend is not the same
  }
}

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

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

You can tune the way the suggestions behave:

• enable_suggestions_on_startup lets you choose to enable or disable "suggest-as-you-type" suggestions on neovim startup. You can then toggle auto suggest with LLMToggleAutoSuggest (see Commands)
• enable_suggestions_on_files lets you enable suggestions only on specific files that match the pattern matching syntax you will provide. It can either be a string or a list of strings, for example:
  • to match on all types of buffers: enable_suggestions_on_files: "*"
  • to match on all files in my_project/: enable_suggestions_on_files: "/path/to/my_project/*"
  • to match on all python and rust files: enable_suggestions_on_files: { "*.py", "*.rs" }

llm.nvim provides the following commands:

• LLMToggleAutoSuggest enables/disables automatic "suggest-as-you-type" suggestions
• LLMSuggestion is used to manually request a suggestion

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

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

Plug 'huggingface/llm.nvim'

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

local llm = require('llm')

llm.setup({
  api_token = nil, -- cf Install paragraph
  model = "bigcode/starcoder", -- the model ID, behavior depends on backend
  backend = "huggingface", -- backend ID, "huggingface" | "ollama" | "openai" | "tgi"
  url = nil, -- the http url of the backend
  tokens_to_clear = { "<|endoftext|>" }, -- tokens to remove from the model's output
  -- parameters that are added to the request body, values are arbitrary, you can set any field:value pair here it will be passed as is to the backend
  request_body = {
    parameters = {
      max_new_tokens = 60,
      temperature = 0.2,
      top_p = 0.95,
    },
  },
  -- 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>",
  tls_skip_verify_insecure = false,
  -- llm-ls configuration, cf llm-ls section
  lsp = {
    bin_path = nil,
    host = nil,
    port = nil,
    version = "0.5.2",
  },
  tokenizer = nil, -- cf Tokenizer paragraph
  context_window = 8192, -- max number of tokens for the context window
  enable_suggestions_on_startup = true,
  enable_suggestions_on_files = "*", -- pattern matching syntax to enable suggestions on specific files, either a string or a list of strings
})