Instructions to use timaeus/dodecahedron-67m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use timaeus/dodecahedron-67m with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="timaeus/dodecahedron-67m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("timaeus/dodecahedron-67m")
model = AutoModelForCausalLM.from_pretrained("timaeus/dodecahedron-67m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use timaeus/dodecahedron-67m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "timaeus/dodecahedron-67m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "timaeus/dodecahedron-67m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/timaeus/dodecahedron-67m

SGLang

How to use timaeus/dodecahedron-67m with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "timaeus/dodecahedron-67m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "timaeus/dodecahedron-67m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "timaeus/dodecahedron-67m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "timaeus/dodecahedron-67m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use timaeus/dodecahedron-67m with Docker Model Runner:
```
docker model run hf.co/timaeus/dodecahedron-67m
```

dodecahedron-67m / README.md

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	---
	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