| # OpenPeerLLM NTK Trainer | |
| ## Model Overview | |
| This package provides a LoRA-free training workflow for OpenPeerLLM-style causal language models by fitting signed log-gate controllers with ntkmirror. It also includes a tinygrad-based gate-controller smoke demo and a benchmark suite that generates charts for quick inspection. | |
| ## Authors | |
| * Andrew Magdy Kamal Nassief | |
| * Riemann Computing Inc. | |
| * OpenPeer AI | |
| ## Intended Use | |
| * Fit sparse forward-pass controllers on top of frozen Hugging Face causal language models. | |
| * Run a low-cost local demo that validates gate training logic with tinygrad. | |
| * Generate benchmark artifacts and charts for performance comparisons. | |
| * Stop the demo run early once the requested accuracy target is reached. | |
| ## Dependencies | |
| * ntkmirror: https://github.com/leochlon/ntkmirror | |
| * Tinygrad: https://github.com/tinygrad/tinygrad | |
| * Optional charting: OpenBB | |
| ## Benchmark Outputs | |
| The benchmark runner records: | |
| * epoch | |
| * training steps | |
| * wall-clock time | |
| * memory usage | |
| * process and thread counts | |
| * samples per second | |
| * initial and final accuracy | |
| * final loss | |
| * predictability score | |
| * learned gate scales | |
| Charts are written as HTML. The benchmark command writes a combined dashboard HTML plus companion charts, prefers OpenBB chart rendering when the optional chart extra is installed, and otherwise falls back to Plotly so the workflow stays runnable. |