https://github.com/jpmcb/nvim-llama

🦙 Ollama interfaces for Neovim
https://github.com/jpmcb/nvim-llama

llama2 neovim nvim nvim-plugin ollama

Last synced: about 1 month ago
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🦙 Ollama interfaces for Neovim

• Host: GitHub
• URL: https://github.com/jpmcb/nvim-llama
• Owner: jpmcb
• License: mit
• Created: 2023-08-26T19:48:17.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-04-06T21:17:03.000Z (about 2 months ago)
• Last Synced: 2024-04-06T22:23:42.001Z (about 2 months ago)
• Topics: llama2, neovim, nvim, nvim-plugin, ollama
• Language: Lua
• Homepage:
• Size: 59.6 KB
• Stars: 203
• Watchers: 12
• Forks: 3
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Lists

• awesome-neovim - jpmcb/nvim-llama - LLM (Llama 2 and llama.cpp) wrappers. (AI / (requires Neovim 0.5))
• awesome-neovim - jpmcb/nvim-llama - LLM (Llama 2 and llama.cpp) wrappers. (AI / (requires Neovim 0.5))
• awesome-neovim - jpmcb/nvim-llama - LLM (Llama 2 and llama.cpp) wrappers. (AI / (requires Neovim 0.5))

README

        
# 🦙 nvim-llama

_[Ollama](https://github.com/jmorganca/ollama) interfaces for Neovim: get up and running with large language models locally in Neovim._

https://github.com/jpmcb/nvim-llama/assets/23109390/3e9e7248-dcf4-4349-8ee2-fd87ac3838ca

🏗️ 👷 Warning! Under active development!! 👷 🚧

## Requirements

Docker is required to use `nvim-llama`.

And that's it! All models and clients run from within Docker to provide chat interfaces and functionality.

This is an agnostic approach that works for MacOS, Linux, and Windows.

## Installation

Use your favorite package manager to install the plugin:

### Packer

```lua

use 'jpmcb/nvim-llama'

```

### lazy.nvim

```lua

{

    'jpmcb/nvim-llama'

}

```

### vim-plug

```lua

Plug 'jpmcb/nvim-llama'

```

## Setup & configuration

In your `init.vim`, setup the plugin:

```lua

require('nvim-llama').setup {}

```

You can provide the following optional configuration table to the `setup` function:

```lua

local defaults = {

    -- See plugin debugging logs

    debug = false,

    -- The model for ollama to use. This model will be automatically downloaded.

    model = llama2,

}

```

### Model library

Ollama supports an incredible number of open-source models available on [ollama.ai/library](https://ollama.ai/library 'ollama model library')

Check out their docs to learn more: https://github.com/jmorganca/ollama

---

When setting the `model` setting, the specified model will be automatically downloaded:

| Model              | Parameters | Size  | Model setting |

| ------------------ | ---------- | ----- | ------------------------------ |

| Neural Chat        | 7B         | 4.1GB | `model = neural-chat`       |

| Starling           | 7B         | 4.1GB | `model = starling-lm`       |

| Mistral            | 7B         | 4.1GB | `model = mistral`           |

| Llama 2            | 7B         | 3.8GB | `model = llama2`            |

| Code Llama         | 7B         | 3.8GB | `model = codellama`         |

| Llama 2 Uncensored | 7B         | 3.8GB | `model = llama2-uncensored` |

| Llama 2 13B        | 13B        | 7.3GB | `model = llama2:13b`        |

| Llama 2 70B        | 70B        | 39GB  | `model = llama2:70b`        |

| Orca Mini          | 3B         | 1.9GB | `model = orca-mini`         |

| Vicuna             | 7B         | 3.8GB | `model = vicuna`            |

> Note: You should have at least 8 GB of RAM to run the 3B models, 16 GB to run the 7B models, and 32 GB to run the 13B models.

70B parameter models require upwards of 64 GB of ram (if not more).

## Usage

The `:Llama` autocommand opens a `Terminal` window where you can start chatting with your LLM.

To exit `Terminal` mode, which by default locks the focus to the terminal buffer, use the bindings `Ctrl-\ Ctrl-n`