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, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("timaeus/dodecahedron-67m")
model = AutoModelForMultimodalLM.from_pretrained("timaeus/dodecahedron-67m")

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

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
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):

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.

Dodecahedron-32M — same architecture scaled down
Timaeus — research organization

Downloads last month: 114

Safetensors

Model size

67.4M params

Tensor type

F32

timaeus
/

dodecahedron-67m

Dodecahedron-67M

Model Details

Training

Checkpoints

Purpose

Related

Dataset used to train timaeus/dodecahedron-67m