Minionese LM
A tiny 1.35M-parameter conversational decoder-only Transformer trained entirely
from scratch on an original Minionese-inspired dataset. It accepts short English
or Minionese messages and replies in a playful, consistent project dialect.
This is an unofficial fan research project. It is not associated with, affiliated
with, or endorsed by Minions or Illumination.
Model details
- Architecture: custom decoder-only causal Transformer
- Parameters: 1,354,560
- Layers: 4
- Hidden size: 160
- Attention heads: 5
- MLP size: 640
- Context length: 256 tokens
- Tokenizer: 512-token byte-level BPE trained from scratch
- Pretrained/base model: none
- Training: 5,000 batches and 9,981,697 sampled non-padding tokens
- Weights: safetensors
- Runtime: PyTorch CPU
The model was trained with MLX on Apple Silicon, but the uploaded weights run with
the included PyTorch implementation on CPU-only Linux, macOS, and Windows systems.
Evaluation
On 400 held-out behavioral prompts, the final model achieved a 99.0% combined
intent/style/clean pass rate and test perplexity of 1.116, compared with a 4.688
unigram baseline. Full machine-readable results are included in evaluation.json.
Usage
1git clone https://huggingface.co/maddiedreese/minionese
2cd minionese
3python3 -m venv .venv
4source .venv/bin/activate
5python -m pip install -r requirements.txt
6python inference.py "hello"
Expected greedy-decoding output:
Python usage:
1from inference import MinioneseRuntime
2
3model = MinioneseRuntime(".")
4print(model.reply("bello, como estas?"))
The canonical training and evaluation source is available at
github.com/maddiedreese/minionese.
Intended use and limitations
This model is intended for education, experimentation, and playful short-form
conversation. It is not an official translator or a general-purpose assistant.
The model supports conversational intents represented in its synthetic training
dataset. It does not understand arbitrary English vocabulary, and compound or
out-of-domain prompts can produce mismatched or malformed responses. Outputs may
also repeat or terminate early because of the model's deliberately tiny size.
The training data does not use pretrained model weights, film subtitles, screenplay
text, private messages, or proprietary Minions dialogue. It was programmatically
generated from an original project dialect.
Hosted demo privacy
The separately hosted demo runs model inference on Railway. Typed chat messages are
not stored. Optional voice interactions are processed by ElevenLabs, as disclosed in
the demo interface. The web application and voice infrastructure are not part of
this model repository.
License
The source code and model artifacts are released under the MIT License. This license
does not grant rights to third-party names, characters, or trademarks.
