sirus/planet-namer

Planet Namer

Planet Namer is a tiny character-level LSTM that generates science-fiction planet names from seven normalized planet attributes. It was built for on-device use in Starbound Exodus and exported as a fixed-shape, single-token ONNX model.

This repository is licensed for non-commercial use only under CC BY-NC 4.0. See LICENSE for the repository-specific notice.

Model details

Property	Value
Architecture	Single-layer character LSTM with stat-conditioned initial state, per-step stat concatenation, and FiLM conditioning
Parameters	226,242
Vocabulary	66 tokens (63 characters plus PAD/SOS/EOS)
Maximum output length	20 characters
Hidden / embedding size	192 / 64
Inputs	Five fixed-shape tensors; see below
Outputs	Next-character logits and recurrent hidden/cell states

The seven input values must be in [0, 1] and in this exact order:

1. atmosphere
2. gravity
3. resources
4. lifesigns
5. temperature
6. water
7. radiation

Files

• planet_namer_fp16.onnx — recommended compact ONNX export (448 KiB)
• planet_namer.onnx — FP32 ONNX export (888 KiB)
• planet_namer_checkpoint.pt — PyTorch state dictionary
• vocab.json — vocabulary, token IDs, stat order, and dimensions
• inference.py — minimal ONNX Runtime command-line example
• train.py — architecture, training, evaluation, and export code

The ONNX model is a single-step recurrent model. On the first step, pass the real stat vector to both stats_init and stats, along with zero h_in and c_in. On later steps, pass zeros to stats_init, keep passing the real values to stats, and feed the previous h_out and c_out back into the model.

Usage

Install the lightweight inference dependencies:

python -m pip install -r requirements.txt

Generate a name from the seven stats:

python inference.py \
  --stats 0.9 0.8 0.7 0.9 0.6 0.8 0.1 \
  --temperature 0.8 \
  --seed 42

Use --temperature 0 for greedy decoding. Higher temperatures increase variation. The helper validates the stat count and range before inference.

The PyTorch checkpoint is a state dictionary, not a serialized executable model. Instantiate PlanetNameLSTM from train.py with a vocabulary size of 66, then load the state dictionary with weights_only=True.

Training data

The model was trained on 1,957 planet and location names collected from these fictional universes: Star Trek, Mass Effect, Warhammer 40,000, Star Wars, Dune, Babylon 5, Halo, Stargate, Firefly, Foundation, and The Expanse. The conditioning values are hand-authored or synthetic metadata. When several names shared the same stat vector, the training pipeline deterministically spread those values using orthographic properties of each name.

The raw name lists are not included in this model repository. Names and marks from the referenced fictional universes may be protected by copyright, trademark, or other rights belonging to their respective owners. This release does not grant rights to any third-party material.

Evaluation

The following results were reproduced from the released checkpoint with seed 42 and the training script's stratified 80/10/10 split (1,564 / 194 / 199):

Metric	Result
Training exact match at temperature 0.1	82.35% (1,288 / 1,564)
Test exact match at temperature 0.1	77.39%
Test mean Levenshtein distance	1.87
Novelty at temperature 1.0	88.5% (200 generated samples)
Generated-vs-training character-bigram KL	0.2901

These are development diagnostics, not a benchmark. In particular, the name-derived collision spreading leaks orthographic information into the conditioning values, making held-out exact-match results optimistic. The novelty result is a single seeded sampling run and will vary.

Intended use

Intended uses are non-commercial creative experiments, games, prototypes, research, and procedural-content tooling where a user wants short fictional planet-name suggestions conditioned on normalized attributes.

The model is not intended for factual astronomy, scientific classification, identity-related naming, or commercial products and services. Review generated names before publication.

Limitations and risks

• The model can reproduce or closely resemble names seen during training.
• Outputs may resemble protected franchise names or marks; novelty is not a clearance check.
• The training corpus is small, English-centric, and dominated by a few fictional universes.
• The stat/name relationship is partly synthetic and should not be interpreted as semantic ground truth.
• Sampling can produce empty, awkward, truncated, or mixed-case strings.
• The ONNX graphs use fixed batch size 1 and fixed recurrent-state dimensions.

Users are responsible for reviewing outputs, respecting third-party rights, providing attribution, and complying with the non-commercial license.

License

The model weights, ONNX exports, vocabulary, and repository-authored code and documentation are released under the Creative Commons Attribution-NonCommercial 4.0 International License. Third-party names and marks are excluded from that grant.