A real fine-tuning path that uses ntkmirror to fit signed log-gate controllers on a frozen Hugging Face causal LLM, and
A tinygrad-backed smoke demo that trains only gate parameters on a synthetic task so the controller idea can be validated locally and cheaply.
A benchmark pipeline that records accuracy, loss, memory, process counts, predictability, and throughput, then renders a combined dashboard plus OpenBB-backed charts.
A runtime GUI for live benchmark runs with current hardware specs baked into the view.
The trainer therefore targets any causal LLM that transformers can load, with OpenPeerAI/OpenPeerLLM as the primary model ID and a smaller fallback for local demos.
The benchmark runner writes a CSV plus HTML charts under artifacts/benchmarks/. The main output is benchmark_dashboard.html, a multi-panel dashboard showing memory, processes, learned gates, loss, predictability, accuracy, training steps, time, and epoch in actual seconds. If the OpenBB charting extension is installed, the companion charts are rendered through OpenBB; otherwise the script falls back to Plotly with the same data.
Launch the runtime GUI
python -m openpeer_trainer.cli gui
The GUI shows the same dashboard, a live benchmark runner, and a hardware-spec table for this computer.
Fit an ntkmirror controller
python -m openpeer_trainer.cli fit --model OpenPeerAI/OpenPeerLLM --train-jsonl train.jsonl --out runs/openpeer_controller.pt
