Llama 3.2 Vision OCR setup with Ollama and Python

This tutorial builds a local LLM workflow for receipt OCR with Llama 3.2 Vision, Ollama and Python. The setup uses a repository-provided setup.yaml file to create a Conda environment. The presenter then installs the Ollama desktop application, downloads the model and checks that it appears in the model list. The walkthrough also covers starting the Ollama service if needed.

The Python script uses requests to call the local API and Base64 to encode the receipt image. Its function accepts a model name, image path and extraction prompt, then builds a JSON payload with an images array. The demonstration processes one receipt image. After the request, the script handles errors and returns the text portion of the response. A timer decorator measures execution time.

The reported receipt test takes around 100 seconds. The presenter says the response captures small print, including the receipt's "save money, live better" line, and identifies receipt sections as well as text. These are findings from the demonstrated test, rather than a general accuracy benchmark.

The speaker assigns the model a B-tier position in the channel's 2026 OCR ranking because of its latency. The tutorial frames it as an option for private document processing on local hardware, while warning that the observed inference time could limit high-volume workflows.