How to run an llm locally guis e

Yes, there are several ways to use local LLMs with a graphical user interface (GUI). Based on the information provided and my knowledge, here are some popular options:

  1. Ollama with Open Web UI Ollama is a tool for running LLMs locally, and Open Web UI provides a ChatGPT-like interface for interacting with these models.

How to set it up:

  • Install Ollama: curl -fsSL https://ollama.com/install.sh | sh
  • Install Docker (required for Open Web UI)
  • Run Open Web UI: 
    docker run -d --name openwebui -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --restart always ghcr.io/open-webui/open-webui:main

  • Access the interface at http://localhost:3000 in your web browser

  • Text Generation WebUI This is a versatile interface that supports various model formats and has many extensions.

How to set it up:

  • Clone the repository: git clone https://github.com/oobabooga/text-generation-webui

  • Follow the installation instructions in the repository's README

  • LM Studio LM Studio provides a clean UI and focuses on GGUF format models.

How to set it up:

  • Download LM Studio from their official website
  • Install and launch the application

  • Use the interface to download and run models

  • Kobold CPP Known for its clean and easy-to-use interface.

How to set it up:

  • Download from: https://github.com/LostRuins/koboldcpp

  • Follow the installation instructions in the repository's README

  • H2O GPT Good for file ingestion and has PDF and web search integration.

How to set it up:

  • Clone the repository: git clone https://github.com/h2oai/h2ogpt
  • Follow the installation instructions in the repository's README

To use these interfaces:

  1. Install the chosen interface following its specific instructions.
  2. Download a compatible LLM (e.g., LLaMA 2, Mistral, GPT-J) through the interface or separately.
  3. Load the model in the interface.
  4. Start interacting with the LLM through the provided GUI.

Remember to monitor your system resources, as running large language models locally can be computationally intensive. Your system with 32GB RAM and a powerful CPU should handle most 7B to 13B parameter models comfortably, but always be prepared to adjust settings or close other applications if needed.

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.