Not every image analysis task needs a 7B+ model. Moondream and BakLLaVA are small, fast vision models that run on hardware where larger vision models struggle — including CPU-only machines and machines with 4–6GB RAM. If you need local image understanding without the overhead of Gemma 3 or LLaVA 7B, these are worth knowing about. … Read more
Ollama’s model library has more coding-specific models than most people realise, and choosing the right one for your hardware and workflow makes a bigger difference than it might seem. General-purpose models like Llama 3.1 or Mistral handle code adequately, but dedicated coding models — trained on much larger and more diverse code corpora — produce … Read more
Qwen 2.5 72B is Alibaba’s largest open-weight model and one of the most capable you can run locally. At 72 billion parameters it’s in the same tier as Llama 3.1 70B and Mixtral 8x22B, but it benchmarks ahead of both on several tasks — particularly coding, mathematics, and multilingual work. If your hardware can handle … Read more
LlamaIndex is one of the most capable frameworks for building RAG systems — retrieval-augmented generation pipelines that let your LLM answer questions grounded in your own documents. Connecting it to Ollama means you get the full LlamaIndex feature set (document loading, chunking, vector indexing, query engines, agents) backed by a local model with no API … Read more
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
