README.md

---
language: en
license: apache-2.0
library_name: pytorch
pipeline_tag: text-generation
tags:
- experimental
- checkpoints
---
# Slayer149 training checkpoints

Automatic recovery archive for controlled data-continuation experiments from
[SlayerLab/Slayer149](https://huggingface.co/SlayerLab/Slayer149).
148,910,738 parameters. Experimental checkpoints are not promoted releases or
verified top-three leaderboard entries. The original release remains unchanged.

`baseline/training-state.pt` and each `runs/<arm-seed>/checkpoint-<step>/training-state.pt`
contain weights, optimizer state, step, training configuration and provenance.
`extension/` contains the longer continuation only if the development selection
criteria pass. Steps are local to each training phase, not total lineage tokens.
Safetensors inference exports are included for the final pilot/extension checkpoints.
These use the accompanying custom PyTorch loader, not Transformers AutoModel.

`reports/` contains development measurements and experiment status. Development
proxies are not GLINT leaderboard results. Full GLINT confirmation, when available,
is explicitly named. Scores and ranking claims must be read with their protocol.
No training corpus or authentication credentials are uploaded.

Restore a trusted `training-state.pt` as `latest.pt` in a new run directory,
restore its matching config and exact tokenized data, then resume the trainer.
Source/tokenizer revisions and data hashes are recorded in the manifests; this
archive does not by itself contain the training datasets.