Ollama

Introduction

A month ago, I uploaded an article in which I installed LM Studio on a MacBook Air and tried local LLM. This time, I tried local LLM with Ollama.

Motivation

I have been studying PINNs and related OpenFOAM for a while, but yesterday there was a big news in LLM area and I decided to use Deep Seek-R1 which had an impact not only on LLM area but also on stock prices. Since I could not use it as it is in my environment, I used a compacted LLM with quantization.

This time, I used Ollama and Open WebUI to use the quantized model from a browser, and I will summarize the contents.

Introduction

It has already been a week since I created the knowledge graph in my local environment and started building the GraphRAG environment. It is finally in a decent working condition. This post is a summary of GraphRAC using Ollama and neo4j that I built in my local environment.

はじめに

In this post, I created a knowledge graph as a starting point for building a RAG system. After trying several different LLMs, only gpt-4o and gpt-4o-mini worked properly. In a recent this post, I summarized the installation and activation of ollama.

I created a knowledge graph using ollama this time, and I summarize its contents here. I hope this will be helpful, as I got into a bit of trouble in some areas.

Introduction

Yesterday in this post I summarized launching ollama in a docker container and using LLMs from JupyterLab. In this post, I summarize launching Open WebUI in a container and using it as a front end to connect from a browser at hand to use ollama’s LLMs.

Motivation

In this post, I mentioned that the LLMs that can build knowledge graphs are OpenAI and Mistral (via API). On the Internet, I have seen examples of GraphRAG environments being built using ollama, as in this post.

I would like to try to build a knowledge graph using LLM in a local environment. In this post, I will summarize the process of installing ollama.