---
language: en
license: apache-2.0
tags:
  - language-model
  - slt
  - singular-learning-theory
  - patterning
  - timaeus
datasets:
  - HuggingFaceFW/fineweb
library_name: transformers
pipeline_tag: text-generation
---

# Dodecahedron-67M

A 67M parameter language model from the Dodecahedron family, trained for singular learning theory (SLT) research.

## Model Details

| Parameter | Value |
|-----------|-------|
| Parameters | 67M |
| Architecture | LlamaForCausalLM |
| Hidden size | 512 |
| Layers | 20 |
| Attention heads | 8 (4 KV heads, GQA 2:1) |
| Head dim | 64 |
| MLP intermediate | 1408 |
| Vocab size | 16,384 |
| Context length | 2,048 |
| Tied embeddings | Yes |

## Training

- **Data**: 40B tokens of [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
- **Tokenizer**: Custom 16k vocab BPE trained on FineWeb (same as Dodecahedron-32M)
- **Optimizer**: AdamW (betas 0.9/0.95, weight decay 0.01, grad clip 1.0)
- **Learning rate**: 3.5e-3 with cosine decay, 1000 warmup steps
- **Batch size**: 524k tokens/step (64 micro × 4 grad accum × 2048 seq)
- **Precision**: bfloat16
- **Hardware**: 8× H100

## Checkpoints

53 checkpoints are available as branches, log-spaced from step 1 to step 9536 (~40B tokens):

```python
from transformers import AutoModelForCausalLM

# Final checkpoint
model = AutoModelForCausalLM.from_pretrained("timaeus/dodecahedron-67m-v2")

# Intermediate checkpoint
model = AutoModelForCausalLM.from_pretrained("timaeus/dodecahedron-67m-v2", revision="step5108")
```

Available steps: 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 19, 22, 25, 30, 35, 40, 47, 55, 65, 75, 88, 103, 121, 141, 165, 193, 225, 263, 308, 359, 420, 491, 574, 671, 785, 917, 1072, 1253, 1465, 1713, 2002, 2340, 2735, 3198, 3738, 4369, 5108, 5970, 6979, 8158, 9536

## Purpose

The Dodecahedron family is designed as small, well-characterized reference models for developmental interpretability and SLT research. Having densely-checkpointed models enables studying learning dynamics, phase transitions, and the geometry of the loss landscape throughout training.

## Related

- [Dodecahedron-32M](https://huggingface.co/timaeus/dodecahedron-32m-v2) — same architecture scaled down
- [Timaeus](https://timaeus.co) — research organization