How to Run Ollama WebUI Chatbot Locally Offline

4 minute read

In today’s connected world, access to powerful language models is often just a click away. However, there are times when internet access is not available, such as during flights, in remote areas, or even underwater (for those adventurous divers). For these situations, having a locally hosted chatbot like Ollama WebUI can be incredibly useful. This guide will walk you through the process of setting up and running Ollama WebUI on your local machine, ensuring you have access to a large language model (LLM) even when offline.

Introduction to Ollama

Ollama WebUI is a versatile platform that allows users to run large language models locally on their own machines. This is particularly beneficial for scenarios where internet access is limited or unavailable. Ollama supports a variety of models, each tailored for different performance and quality needs. Below is a table detailing the available models, their parameters, size, compute power required, accuracy expectation, and quality.

Model Parameters Model Size (approx. in GB) Compute Power Needed Accuracy Expectation Quality
llama3 13B 6.5GB Moderate High Good
llama3:70b 70B 35GB High Very High Excellent
phi3 12B 6GB Moderate High Good
mistral 7B 3.5GB Low Moderate Fair
neural-chat 6B 3GB Low Moderate Fair
starling-lm 11B 5.5GB Moderate High Good
codellama 14B 7GB Moderate High Good
llama2-uncensored 13B 6.5GB Moderate High Good
llava 20B 10GB High Very High Excellent
gemma:2b 2B 1GB Low Moderate Fair
gemma:7b 7B 3.5GB Low Moderate Fair
solar 9B 4.5GB Moderate High Good

System Requirements

To run these models effectively, you will need a computer with the following minimum specifications:

  • CPU: Multi-core processor (Quad-core or better recommended)
  • RAM: At least 16GB of RAM (more for larger models)
  • Storage: Sufficient free disk space to accommodate the model size (e.g., 10GB+)
  • Operating System: Windows, macOS, or Linux

Table of Contents

To-Do List

  1. Download and install Ollama.
  2. Clone the repository.
  3. Install dependencies.
  4. Run the application.
  5. Optionally, use Docker for easier setup.

Getting Started ✅

To get started with Ollama WebUI, you’ll first need to ensure you have Python installed on your system. Once that’s done, you can proceed with downloading Ollama here and cloning the repository.

Cloning the Repository 📥

Run the following command to clone the Ollama WebUI repository:

git clone

Installing Dependencies 🔧

Navigate to the project directory:

cd ollama-webui

Install the necessary dependencies:

pip install -r requirements.txt

Running the Application 🚀

To start the application, use the following command:


Once the application is running, you can access it in your web browser at:



Change models

In the booton of the webapp there is and

Using Docker 🐳

If you prefer using Docker, follow these steps. Docker simplifies the setup process by encapsulating the application and its dependencies into a single container.


Make sure you have Docker installed on your machine. You can download Docker from Docker’s official website.

Running the Docker

Pull the Docker image from Docker Hub:

docker pull ruslanmv/ollama-webui

Run the application using Docker:

docker run -p 1338:1338 ruslanmv/ollama-webui

Access the application in your web browser at:



When you’re done using the application, you can stop the Docker container with the following command:

docker stop <container-id>

Incorporated Projects

Ollama WebUI incorporates several projects to provide a seamless and robust experience. The primary focus is to offer an easy-to-use interface for running large language models locally, without the need for continuous internet access.

Before using Ollama WebUI, ensure you comply with any applicable laws and regulations related to the use and distribution of large language models. This software is provided “as is,” without warranty of any kind.

Using Ollama on the Terminal

While the web-based interface of Ollama WebUI is user-friendly, you can also run the chatbot directly from the terminal if you prefer a more lightweight setup. This can be particularly useful for advanced users or for automation purposes.

Running Ollama without the WebUI

To run Ollama directly from the terminal, follow these steps:

  1. Navigate to the Project Directory:
    cd ollama-webui
  2. Run the Model: Use a Python script or an interactive Python session to load and interact with the model. For example:
    python Hello how are you?

    or if you want customize you own code you can use:

     import ollama
     from langchain_community.llms import Ollama
     # Replace with the desired model name
     model_name = "llama3" 
     #Check if you have the model
     !ollama list
     # Download the model 
     #Load the model
     llm = Ollama(model=model_name)
     prompt='''Hello, how can I help you?'''
         # Interact with the model
     response = llm.invoke(prompt)
  3. Ensure Dependencies are Met: Ensure you have all necessary dependencies installed, including the specific model files and any required libraries.

While this method requires a bit more setup and knowledge of Python programming, it offers greater flexibility and can be integrated into various workflows or applications without relying on a web browser.

By following these steps, you can have Ollama WebUI running on your local machine, ready to assist you with various tasks even when you’re offline. Whether you’re in an airplane, deep in the forest, or anywhere else without internet access, you’ll have a powerful language model at your fingertips. Happy chatting!


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