---
language:
- en
license: other
library_name: transformers
pipeline_tag: fill-mask
tags:
- babylm
- strict-small
- masked-language-modeling
- factorized
- ltg-bert
---

# FACTORIZED Natural Dense

Selected strict-small checkpoint from the controlled FACTORIZED study. The
model uses an LTG-BERT-style encoder trained with whole-word masking and an
auxiliary replaced-token objective. Its 384-dimensional representation is
partitioned into three 128-dimensional factor readouts. In this natural-dense
control the factor readout is dormant during language-model inference.

## Model summary

| Property | Value |
|---|---:|
| Parameters | approximately 33.3M |
| Layers / hidden size | 12 / 384 |
| Attention heads | 6 |
| FFN size | 1,280 |
| Context length | 512 |
| Tokenizer | byte-level BPE, 16,384 tokens |
| Training corpus | BabyLM strict-small, conservatively counted as 10M words |
| Total exposure | 100M words |
| Optimizer | LAMB |
| Peak learning rate | 3.5e-3 |

## Usage

```python
from transformers import AutoModelForMaskedLM, AutoTokenizer

repo_id = "miguelcsx/factorized-natural-dense"
revision = "chck_100M"

tokenizer = AutoTokenizer.from_pretrained(repo_id, revision=revision)
model = AutoModelForMaskedLM.from_pretrained(
    repo_id,
    revision=revision,
    trust_remote_code=True,
)
```

Remote code is required for the custom TOLM architecture. Review `tolm.py`
before loading it in a security-sensitive environment.

## Development evaluation

The selected `chck_100M` checkpoint led the FACTORIZED tournament on the
pre-registered shared full-task mean among the three finalists.

| Evaluation | Score |
|---|---:|
| BLiMP | 70.01 |
| BLiMP Supplement | 60.63 |
| Entity Tracking | 17.70 |
| COMPS | 51.41 |
| GlobalPIQA | 36.09 |

These are development measurements, not a claim of an official leaderboard
placement. The complete official BabyLM 2026 evaluation is being regenerated
for submission.

## Checkpoint revisions

The repository contains all strict-small challenge revisions:
`chck_1M` through `chck_10M`, then `chck_20M` through `chck_100M`.
The final submission checkpoint is `chck_100M`.

## Provenance

- Final checkpoint model SHA-256:
  `ba89940028a902f4aa09385966b8e83bb346a0abaab6899feec1fa2d3a4b7e13`.
- Evaluation backend: `mntp`.
- Fast-evaluation parity maximum absolute logit delta: `0.0`.
- Training and compliance details are recorded in `training_manifest.json`.

## Limitations

This is a small English developmental language model trained for controlled
BabyLM experiments. It is not a general-purpose assistant. The factorized
readout in this control is dormant during ordinary masked-language-model
inference.