LLM powered development for IntelliJ

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

Note

When using the Inference API, you will probably encounter some limitations. Subscribe to the PRO plan to avoid getting rate limited in the free tier.

https://huggingface.co/pricing#pro

Features

Code completion

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

Choose your model

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.

Always fit within the context window

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

Configuration

Endpoint

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. Set API token = <your token> in plugin settings
   2. (not supported yet) You can define your HF_HOME environment variable and create a file containing your token at $HF_HOME/token
   3. (not supported yet) 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 set Model = <model identifier> in plugin settings

With your own HTTP endpoint

All of the above still applies, but note:

• When an API token is provided, 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

Models

llm-intellij is assumed to be compatible with any model that generates code.

Here are some configs for popular models in JSON format that you can put in your Settings (Cmd+, > LLM Settings)

Starcoder

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

Note

These are the default config values

CodeGemma

{
   "tokensToClear": [
      "<|file_separator|>"
   ],
   "fim": {
      "enabled": true,
      "prefix": "<|fim_prefix|>",
      "middle": "<|fim_middle|>",
      "suffix": "<|fim_suffix|>"
   },
   "model": "google/codegemma-2b",
   "context_window": 8192,
   "tokenizer": {
      "repository": "google/codegemma-2b"
   }
}

CodeLlama

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

Note

Spaces are important here

llm-ls

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

"$HOME/.cache/llm_intellij/bin"

When developing locally or if you built your own binary because your platform is not supported, you can set the llm-ls > Binary path setting to the path of the binary.

llm-ls > Version is used only when llm-intellij downloads llm-ls from the release page.

You can also set the log level for llm-ls with llm-ls > Log level, which can take any of the usual info, warn, error, etc as a value. The log file is located in:

"$HOME/.cache/llm_ls/llm-ls.log"

Tokenizer

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:
• from a local file on your disk:
• from a Hugging Face repository, llm-ls will attempt to download tokenizer.json at the root of the repository:
• from an HTTP endpoint, llm-ls will attempt to download a file via an HTTP GET request: