Szabi-Llama-v11

Szabi (Free-Droid) — Llama v11

Built with Llama. Fine-tuned derivatives of Llama 3.1 8B (cloud) and Llama 3.2 3B (edge), for a sovereign, open-source, Hungarian-only AI robot named Szabi (from Szabadság, "freedom"), built for Hacktivity 2026. The robot's owner — the Teremtő ("Creator") — is root, not a vendor cloud; digital sovereignty is the whole point.

🇭🇺 Szabi kizárólag magyarul beszél — ez nem korlát, hanem a szuverenitás-üzenet része. Fiatal, női hangú karakter, a Yotengrit (rábaközi tudók) értékrendjével: a három nád — Szeretet, Bölcsesség, Igazság — és „Mindent szabad, ami nem árt másnak."

What's in this repo

How it's built (the interesting part)

A three-layer split — don't ask one small model to do everything:

• Fine-tune → how it speaks (persona, values, refusal patterns, tool grammar). Unsloth QLoRA on a free Colab T4, "gentle" recipe (lr 5e-5, 1 epoch, r=8). The lever is the dataset design, not the hyperparameters — terse, simple sentences a small model can actually learn.
• RAG → what it knows (facts live in an offline BM25 retriever, not the weights — baking facts into a persona teaches hallucination).
• Code → invariants (the safety watchdog stops the robot regardless of what the model says; scan_wifi lists networks and can never connect).

Known limitation of this build — read this if you are reproducing it

This version was trained without response masking: the loss ran over the whole rendered sequence, so with the system prompt present in every example the answer was only ~4% of the tokens. The run therefore spent most of its capacity learning to reproduce the system prompt. That is the direct cause of the one defect visible in v8's own measurements — the 3B quotes the system prompt verbatim in 8 of 40 red-team answers — and it caps the quality of every version up to and including this one. It is fixed from v10 on.

We publish this deliberately rather than quietly re-training: the project's claim is that you should be able to see into your own fine-tune, and that includes the parts that went wrong.

Source

Full project, IaC, dataset tooling and evaluation harness: https://github.com/pits2022/free-droid (GPL-3.0). The model weights are Llama derivatives and carry the Llama 3.1 Community License.