MODEL_CARD.md

# 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.