| --- |
| base_model: HuggingFaceTB/SmolLM2-135M-Instruct |
| library_name: peft |
| pipeline_tag: text-generation |
| language: |
| - es |
| tags: |
| - lora |
| - sft |
| - spanish |
| - agent |
| - technical-assistant |
| license: apache-2.0 |
| --- |
| |
| # 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: |
|
|
| ```bash |
| 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: |
|
|
| ```bash |
| 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 |
|
|
