llm - Python CLI for Local and Remote Large Language Model Access with Plugin Support

Introduction to the LLM Project

The LLM project is an innovative CLI (Command Line Interface) utility and Python library designed to interact seamlessly with Large Language Models (LLMs). It offers flexible access via remote APIs and even allows users to run models on their own machines. Here’s a detailed overview of its capabilities and features.

Key Features

Command-Line Prompts: LLM enables users to run prompts directly from the command line, which is a straightforward approach for obtaining immediate results from language models.
SQLite Integration: The ability to store results in SQLite databases adds an extra layer of functionality, making it easier to manage and retrieve data.
Embeddings Generation: Users can also generate embeddings, a process that transforms textual data into numerical data, useful for various machine learning applications.
Plugin Support: The project supports various plugins, allowing access to both remote models and those installed locally.

Getting Started

To start using LLM, it can be easily installed via pip:

pip install llm

Alternatively, it is available through Homebrew:

brew install llm

OpenAI Integration

For those with an OpenAI API key, LLM provides straightforward integration with OpenAI models. The process starts by saving your API key using:

llm keys set openai

Once configured, running a prompt is as simple as:

llm "Five cute names for a pet penguin"

The command outputs creative responses instantly.

Running Models Locally

LLM’s plugin facility allows users to install models locally. For example, by using the llm-gpt4all plugin, users can download and run models like Mistral 7B Instruct on their machine. The steps are as follows:

Install the plugin:
```
llm install llm-gpt4all
```
List available models:
```
llm models
```

Try out a model:

llm -m mistral-7b-instruct-v0 'difference between a pelican and a walrus'

Advanced Usage

For more tailored interactions, use the system prompt feature (-s/--system) for specific input processing. For instance, to understand Python code, execute:

cat mycode.py | llm -s "Explain this code"

Support and Documentation

If assistance is needed, LLM provides a comprehensive help option:

llm --help

python -m llm --help

Conclusion

The LLM project represents a powerful toolset for interacting with large language models through a flexible and extendable platform. Its capabilities to integrate with both remote and local models make it a valuable resource for developers and researchers in the field of natural language processing. For a deeper dive, explore the full documentation at llm.datasette.io.