Ollama

DeepSeek’s first-generation of reasoning models with comparable performance to OpenAI-o1, including six dense models distilled from DeepSeek-R1 based on Llama and Qwen.

Here is the list and examples of the most useful Ollama commands (Ollama commands cheatsheet) I compiled some time ago. Hopefully it will be useful to you.

Not long ago was released. Let’s catch up and test how Mistral Small performs comparing to other LLMs.

Reranking is a second step in Retrieval Augmented Generation (RAG) systems, right between Retrieving and Generating.

Testing how Perplexica performs with various LLMs running on local Ollama: Llama3, Llama3.1, Hermes 3, Mistral Nemo, Mistral Large, Gemma 2, Qwen2, Phi 3 and Command-r of various quants and selecting The best LLM for Perplexica

Awesome food is the pleasure for your eyes too. But in this post we will compare two AI-based search systems, Farfalle and Perplexica.

That’s very exciting! Instead of calling copilot or perplexity.ai and telling all the world what you are after, you can now host similar service on your own PC or laptop!

Recently we have seen several new LLMs were released. Exciting times. Let’s test and see how they perform when detecting logical fallacies.

When I started experimenting with LLMs the UIs for them were in active development and now some of them are really good.

Still there are some common approaches how to write good prompts so LLM would not get confused trying to understand what you wand from it.

Testing how models with different number of parameters and quantization are behaving.

After installing ollama better to reconfigure ollama to store them in new place right away. So after we pull a new model, it doesn’t get downloaded to the old location.