Artem X 

Meta-Spider Framework — https://codeberg.org/imperius/meta-spider
Documentation on Codeberg Pages (RU, EN)  - https://imperius.codeberg.page/meta-spider/

Meta-Qwen-4b (the wrapper for Qwen-4b) - https://huggingface.co/Imperius/Meta-Qwen3.5-4B
Meta-Granite-8b (the wrapper for Granite-8b) - https://huggingface.co/Imperius/meta-granite-3.3-8b-instruct

This is a direct sequel to the article “meta-attention is all you need”. Reading it first is recommended but not required — a tour of the architecture is included below.

The first article described the idea in detail and published the experiment sources, but “sources” there meant simply all the code that had been written, with no system to it — including several different implementations of the same components.

So here is a framework with a ready-made toolkit you can try out on LLMs, including in agentic scenarios.

Ready-to-use, lightweight trained wrappers are also provided — one tiny model (Qwen-3.5–4B) and one mid-size one (Granite 3.3 8B). All of them can be run through llama.cpp.
https://medium.com/@artem-x/building-the-meta-spider-framework-on-top-of-meta-attention-476eb95bb85b?postPublishedType=initial

Why? To create Skynet, of course.

Well, also because I wanted to understand, in detail, what this field that fascinates me so much is breathing with right now. And the best way to understand something is to try to explain it to someone else.

Besides that, I want to move into deep learning professionally, and publishing my interesting projects on the internet seems like the fastest way to get noticed.

Personally, I enjoyed the process a lot, and I invite Habr readers to dive into this small journey with me.

Links to the dataset, weights, and code are attached at the end of the article. The dataset and weights are on Hugging Face; the codebase is on Codeberg, a GitHub-like platform with a similar workflow.

Let's go.

I will describe how I trained Google's 270M-parameter Gemma-3 language model to control a tracked robot with a robot arm in the MuJoCo environment, using natural-language commands from a human.

It can move freely around the map, go forward and backward, turn left and right, grab objects, and put them down.
https://dev.to/imperius_903049e65aa91ec5/how-i-loaded-a-compact-open-llm-into-a-robot-and-told-it-to-walk-and-grab-things-4h40

In this short essay, I want to reflect on how AI is depicted in fiction, or more specifically, on intelligent machines capable of solving all intellectual tasks at a human level or better.

Why are the first two Terminator films still the most realistic depiction of AI in fiction? What does James Cameron's technical background have to do with it? And why are intelligent computers almost always portrayed as "silicon humans"?

A story about a mid-1990s AI research project that its author packaged as a computer game.

A short essay on why the developer community should invest as much effort as possible into LLMs that are free from corporations and states.

What if an LLM could observe its own hidden states? Meta-transformers use cognitive tokens and meta-attention to turn internal uncertainty into usable signal.
https://dev.to/imperius_903049e65aa91ec5/meta-attention-is-all-you-need-1797