Omegus

Omegus is a Spanish technical chatbot model package for the Charlie / Omega agent architecture.

It is designed to respond as a precise technical assistant with progressive status reporting, software architecture judgment, and clear explanations of the Omega framework.

This repository currently contains a first local demo LoRA adapter. It is intentionally small and should be treated as a prototype checkpoint, not a production-quality assistant yet.

Source material:

• ../Charlie-Skill.md
• ../spec_maestra_framework_unificado_v0.3.md

The published adapter fine-tunes HuggingFaceTB/SmolLM2-135M-Instruct with LoRA using a compact chat dataset in data/charlie_omega_sft.jsonl.

Training Run

First published adapter:

• Base model: HuggingFaceTB/SmolLM2-135M-Instruct
• Method: LoRA SFT
• Local hardware: Apple Silicon MPS
• Dataset size: 20 chat examples
• Epochs: 1
• Train loss: 3.502
• Eval loss: 3.588

Hugging Face Jobs training was attempted, but the account did not have enough prepaid credit balance at the time. The current adapter was trained locally instead.

Intended Use

• Spanish technical chatbot
• Software architecture and code-review assistant behavior
• Omega framework explanation and synthesis
• Prototype agent-persona research

Local Dry Run

From this folder:

uv run train_sft.py

This trains locally if your machine has the needed compute.

Push To Hugging Face

The default Hub target is TOKETTER/Omegus. With a logged-in Hugging Face session:

export HUB_MODEL_ID="TOKETTER/Omegus"
uv run train_sft.py

The script pushes LoRA adapter/checkpoints to the Hub when HUB_MODEL_ID is set.

Quick Load

from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

repo = "TOKETTER/Omegus"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoPeftModelForCausalLM.from_pretrained(repo)

Recommended Cloud Job Shape

Default Hub target:

TOKETTER/Omegus

For a cheap demo on Hugging Face Jobs:

• Flavor: t4-small or similar low-cost GPU
• Timeout: 1h
• Base model: HuggingFaceTB/SmolLM2-135M-Instruct

For a better small assistant:

• Flavor: a10g-large
• Timeout: 2h
• Increase dataset size before training

Next Dataset Upgrade

The included dataset is intentionally small so the training pipeline is easy to inspect. The next quality step is to expand it into 200-500 instruction examples extracted from the two source docs, with separate examples for:

• Charlie activation and progressive logging
• Code review and bug triage behavior
• Ω framework explanations
• Ω6 functional consciousness caveats
• Mathematical definitions and architecture summaries