README.md

metadata

title: LBW Guard HF Evaluation Runner
emoji: 🚀
colorFrom: green
colorTo: blue
sdk: gradio
python_version: '3.12'
app_file: app.py
suggested_hardware: t4-medium
models:
  - TinyLlama/TinyLlama_v1.1
  - Qwen/Qwen2.5-0.5B
datasets:
  - Salesforce/wikitext
tags:
  - optimizer
  - training
  - gradio
  - gpu

Copyright (c) Qluon Inc. All rights reserved.

Provided for Learn-By-Wire Guard evaluation and customer testing under the applicable Qluon license terms.

LBW Guard HF Evaluation Runner

This private Hugging Face Space provides two GPU-ready LBW Guard evaluation runners:

• Quick Comparison: a short AdamW vs lbw_guard WikiText LoRA run based on LBW_Guard_Easy_Test_COLAB.ipynb.
• Ablation Matrix: a scenario sweep for optimizer, learning rate, schedule, steps, data size, and LoRA rank based on LBW_Guard_Ablation_Test_COLAB.ipynb.

Both runners produce JSON and CSV artifacts in the Space working directory. The quick runner writes final metrics plus LBW-vs-AdamW gains. The ablation runner writes per-scenario metrics plus LBW-vs-AdamW gains for each scenario.

It installs lbw-guard from PyPI and does not vendor the local lbw/ source folder.

Related Paper

Learn-by-Wire Training Control Governance: Bounded Autonomous Training Under Stress for Stability and Efficiency

• Author: Anis Radianis
• arXiv: https://arxiv.org/abs/2605.19008
• DOI: https://doi.org/10.48550/arXiv.2605.19008

If you use these runners or the lbw-guard package in evaluation, please cite the paper above.

Use GPU hardware for meaningful runtime. CPU can load the app, but training is intentionally capped to tiny smoke settings so users do not accidentally start long CPU jobs.

The app writes run artifacts to the Space working directory. Add persistent storage if you need outputs to survive Space restarts.