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:

bash

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

python

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