llama.vim

Local LLM-assisted text completion.

---

Features

• Auto-suggest on cursor movement in Insert mode
• Toggle the suggestion manually by pressing Ctrl+F
• Accept a suggestion with Tab
• Accept the first line of a suggestion with Shift+Tab
• Control max text generation time
• Configure scope of context around the cursor
• Ring context with chunks from open and edited files and yanked text
• Supports very large contexts even on low-end hardware via smart context reuse
• Speculative FIM support
• Speculative Decoding support
• Display performance stats

Installation

Plugin setup

• vim-plug

  Plug 'ggml-org/llama.vim'

• Vundle

  cd ~/.vim/bundle
  git clone https://github.com/ggml-org/llama.vim

  Then add Plugin 'llama.vim' to your .vimrc in the vundle#begin() section.

• lazy.nvim

  {
      'ggml-org/llama.vim',
  }

Plugin configuration

You can customize llama.vim by setting the g:llama_config variable.

Examples:

1. Disable the inline info:

   " put before llama.vim loads
   let g:llama_config = { 'show_info': 0 }

2. Same thing but setting directly

   let g:llama_config.show_info = v:false

3. Disable auto FIM (Fill-In-the-Middle) completion with lazy.nvim

   {
       'ggml-org/llama.vim',
       init = function()
           vim.g.llama_config = {
               auto_fim = false,
           }
       end,
   }

4. Changing accept line keymap

   let g:llama_config.keymap_accept_full = "<C-S>"

Please refer to :help llama_config or the source for the full list of options.

llama.cpp setup

The plugin requires a llama.cpp server instance to be running at g:llama_config.endpoint.

Mac OS

brew install llama.cpp

Windows

winget install llama.cpp

Any other OS

Either build from source or use the latest binaries: https://github.com/ggml-org/llama.cpp/releases

llama.cpp settings

Here are recommended settings, depending on the amount of VRAM that you have:

• More than 64GB VRAM:

  llama-server --fim-qwen-30b-default

• More than 16GB VRAM:

  llama-server --fim-qwen-7b-default

• Less than 16GB VRAM:

  llama-server --fim-qwen-3b-default

• Less than 8GB VRAM:

  llama-server --fim-qwen-1.5b-default

Use :help llama for more details.

Recommended LLMs

The plugin requires FIM-compatible models: HF collection

Examples

Using llama.vim on M1 Pro (2021) with Qwen2.5-Coder 1.5B Q8_0:

The orange text is the generated suggestion. The green text contains performance stats for the FIM request: the currently used context is 15186 tokens and the maximum is 32768. There are 30 chunks in the ring buffer with extra context (out of 64). So far, 1 chunk has been evicted in the current session and there are 0 chunks in queue. The newly computed prompt tokens for this request were 260 and the generated tokens were 24. It took 1245 ms to generate this suggestion after entering the letter c on the current line.

Using llama.vim on M2 Ultra with Qwen2.5-Coder 7B Q8_0:

llama.vim-0-lq.mp4

Demonstrates that the global context is accumulated and maintained across different files and showcases the overall latency when working in a large codebase.

Another example on a small Swift code

Implementation details

The plugin aims to be very simple and lightweight and at the same time to provide high-quality and performant local FIM completions, even on consumer-grade hardware. Read more on how this is achieved in the following links:

• Initial implementation and technical description: ggml-org/llama.cpp#9787
• Classic Vim support: ggml-org/llama.cpp#9995

Other IDEs

• VS Code: https://github.com/ggml-org/llama.vscode