Ollama exposes a clean REST API that lets you interact with any locally running model from your Python code — no paid API keys, no rate limits, nothing leaving your machine. Whether you’re building a chatbot, automating document processing, or integrating LLM capabilities into an existing application, the Ollama API is your bridge between Python … Read more
Command R+ is Cohere’s flagship open-weight model and one of the most capable models you can run locally for RAG (retrieval-augmented generation) and tool use. It was specifically trained with these workflows in mind — it handles long documents better than most models its size, supports structured tool calling natively, and performs well in multi-turn … Read more
LLaVA (Large Language and Vision Assistant) was one of the first open-source multimodal models that could meaningfully analyse images locally. You give it an image alongside a text prompt and it responds based on what it sees. While newer models like Gemma 3 have surpassed it on benchmarks, LLaVA remains widely used and is one … Read more
One of the most common surprises when using Ollama is discovering that your model has a context length of 2048 tokens by default — even if the model itself supports 128K. Ollama’s default is conservative to protect users on limited hardware, but for most people the right context length is much higher. This guide explains … Read more
Mixtral 8x7B was a landmark release when it came out — a Mixture of Experts model that delivers performance competitive with much larger models by selectively routing each token through just two of its eight expert networks at inference time. This means you get the quality of a large model at the computational cost of … Read more
Once you have Ollama running and a few models downloaded, you’ll spend a surprising amount of time managing those models — pulling new ones, removing ones you no longer use, checking what’s loaded, switching between versions. The Ollama CLI has a clean set of commands for all of this, and knowing them well saves you … Read more
Whether you’re troubleshooting a broken install, switching to a different setup, or just freeing up disk space, uninstalling Ollama is slightly more involved than most applications. The binary and the service are one thing — the model files (which can be tens or hundreds of gigabytes) are stored separately and don’t get removed by a … Read more
Running Ollama behind a reverse proxy gives you things the raw API doesn’t: a clean domain name instead of an IP and port, HTTPS so connections are encrypted, basic authentication to keep your models private, and the ability to put Ollama on a standard port (80/443) that doesn’t need special firewall rules. If you’re running … Read more
Ollama’s behaviour is almost entirely configurable through environment variables — where it stores models, what address and port it listens on, how long it keeps models in memory, how many parallel requests it handles. The defaults are sensible for a single-user local setup, but as soon as you want to customise anything — move models … Read more
You’ve installed Ollama, you try to run a model, and nothing happens — or you get a cryptic error you’ve never seen before. This is frustrating, but nearly every Ollama startup issue falls into one of a handful of root causes. This guide works through them systematically, from the most common to the more obscure, … Read more
