---
language:
- en
library_name: mlx
pipeline_tag: text-generation
tags:
- nero
- mlx
- optimizer-research
- navier
---

# Nero Optimizer Work — Navier

This repository contains the final MLX checkpoint for the **Navier** arm of
the Nero Optimizer Work research sweep.

## Summary

- Optimizer: navier
- Requested learning rate: the run's configured default/controller schedule
- Training budget: **500,000,000 tokens**
- Context length: **128 tokens**
- Backend: **Apple MLX**
- Model family: matched dense-deep decoder, 2,048-token vocabulary, 128-wide residual stream, 6 blocks, 32-dimensional attention heads, 148-wide gated MLP
- Stored model parameters: approximately **999,680**
- Final training loss: **3.339486**
- Final logged throughput: **363,230 tokens/s**
- Tail throughput: **363,332 tokens/s (median of the final logged samples)**
- Final tokens seen: **500,000,000**

## Files

- model.npz — final MLX model weights from checkpoint_000500000000
- state.json — checkpoint state metadata
- run.json — frozen run configuration
- metrics.jsonl — complete training metrics log
- config.json — model and publication metadata

## Evaluation

The figures above are training-run measurements. An independent held-out
validation artifact was not saved with these runs, so this card does not claim
a validation score. Compare checkpoints using the same frozen evaluation pass
before drawing quality conclusions.

## Intended use and limitations

This is an experimental research checkpoint, not an instruction-tuned or
production-ready language model. It is published to make the optimizer
comparison reproducible. The raw MLX weights require a compatible local MLX
loader and are not a Transformers checkpoint.

## Reproducibility

All arms use the same prepared finephrase-balanced-500m-2k-v2 token stream,
128-token context, 32-example batches, and 500M-token target. The full local
training log is included in metrics.jsonl.

## License

No new model license is asserted by this experimental publication. Review the
source-data terms before redistribution or downstream use.