Who is Llama Parse ?

Sure! Here’s a fun intro to LlamaParse:

Imagine you have a mountain of documents—PDFs, messy scans, or text-heavy reports—and you need to extract useful information fast. Instead of manually copying and pasting like a tired intern, meet LlamaParse, your AI-powered document whisperer!

LlamaParse takes complex, unstructured documents and turns them into clean, structured data, ready for analysis or automation. Whether you’re dealing with legal contracts, research papers, or financial reports, this Llama doesn’t spit—it delivers.

This an experiment to reuse the same principles for accessing Ollama via Clojure, this time with ClojureScript.

Basic routing diagram to show the request going from the browser all the way to the ollama model.

As part of getting into SLM for small AI devices, we are going to look at inference speed on the Raspberry Pi. Mostly six models are of interest:

• tinyllama
• tinydolphin
• phi3
• smallthinker
• granite3.1-moe
• llama3.2:1b

Each model has a small number of parameters, to make sure we get usable speeds for inference.

We will get speed figures on simple inference for each of them.

In a previous post, we installed Arch Linux on the raspberry pi (make sure to have a look at Running Arch on Pi ).

I have some software packages on the Arch User Repositry (AUR), making it easy to deploy on various devices. It used to be a bit tricky to install Arch on the raspberry pi, but now it is possible to get to a running system quite fast, so here are notes on how to get going, and get your Pi running with Arch.

Download the ISO

First of all, we need the ISO for install. The iso/zip I downloaded was from rasparch on sourceforge. Directly burned the file to the SD card, using standard tools Etcher. I inserted the SD card in the Pi, plug the power, and the small beast started booting …

Back to Clojure for a bit with this library to access Ollama via my favourite language. Sheer amount of reliable Java+Clojure open source libraries has just made my life easier in the last few weeks.

Basically, Pyjama has full support for all the Ollama API endpoints, generate, chat, pull etc.. you name it.

Right now it is available as a clojure deps library, you can use it in your project, by adding the git coordinates to the deps.edn file.

The Goal was to have a go program that converts JSON data i have on a project to a CSV file. Looks like that was easy for ChatGPT so did a few extra steps to generate some Golang code, first naively, then streaming through the input json file. What follows is the ChatGPT conversation.

You: turn this json snippet into csv: [ { “Id”: 0, “Date”: “2022-11-01T09:00:00+09:00”, “Project”: null, “Cost”: 5000, “Estimate”: 6000, “Updated”: “2022-11-01T00:00:00Z” }, { “Id”: 0, “Date”: “2022-10-01T09:00:00+09:00”, “Project”: null, “Cost”: 4000, “Estimate”: 5000, “Updated”: “2022-11-01T00:00:00Z” } ]