awesome-local-llm

🥈Silver

A curated list of platforms, tools, and resources to run LLMs locally. Helps operations teams deploy and manage self-hosted AI models. Connects to inference platforms and engines for seamless integration.

1,086870Updated 1w ago

Intermediate30min to implementautomation

Saves ~30 min per use

Quick InstallView Source

git clone https://github.com/rafska/awesome-local-llm.git

Works with:

Claude

Overview

About This Skill

https://github.com/awesome-local-llm

Use Cases

Set up a local LLM for developing custom AI applications without cloud dependency.

Evaluate and compare various LLMs using benchmarks and leaderboards provided in the resources.

Automate the deployment of LLMs on personal hardware for testing and development purposes.

Utilize user-friendly interfaces to streamline interactions with locally hosted AI models.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/rafska/awesome-local-llm

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Provide a curated list of awesome platforms, tools, practices, and resources for running large language models (LLMs) locally. Include details about [PLATFORMS], [TOOLS], and [BEST PRACTICES]. Additionally, suggest [RESOURCES] for further learning and implementation.

Example Output

Here’s a curated list of platforms and tools for running LLMs locally:

1. **Platforms**: 
   - **Hugging Face Transformers**: A popular library for natural language processing tasks, offering pre-trained models and easy integration for local deployment.
   - **OpenAI's GPT-2**: A versatile model that can be fine-tuned for specific tasks, available for local setups.

2. **Tools**: 
   - **Docker**: Use Docker containers to easily manage dependencies and environments for your LLMs.
   - **NVIDIA CUDA**: Essential for leveraging GPU acceleration, making local LLM training and inference significantly faster.

3. **Best Practices**: 
   - Always start with a smaller model to test your local setup before scaling up.
   - Monitor memory usage to avoid crashes during training or inference.

4. **Resources**: 
   - **GitHub Repositories**: Explore repositories like `awesome-llms` for community-contributed resources.
   - **Online Courses**: Platforms like Coursera or Udacity offer courses on deploying machine learning models locally. 

This list aims to empower developers and researchers looking to harness the power of LLMs without relying on cloud services, ensuring greater control and customization.