VORTEX

Vehicle Ontology Resolution Through EXplanatory Compression

A 25MB transformer that learned how VINs encode vehicle identity.

Results

Make accuracy: 100%
Model accuracy: 99%
Trim accuracy: 90%

Architecture

• 6-layer decoder-only transformer
• 6.6M parameters
• 256-dim embeddings
• Trained on 50K examples in 8 minutes

Usage

fromllm.modelimportVortexLLMfromllm.tokenizerimportVortexTokenizerimporttorch# Loadtokenizer=VortexTokenizer.load("models/vortex_llm.tokenizer.json")
checkpoint=torch.load("models/vortex_llm.pt", map_location="cpu")
model=VortexLLM(
vocab_size=checkpoint['vocab_size'],
dim=checkpoint['dim'],
n_layers=checkpoint['n_layers'],
pad_id=tokenizer.pad_id
)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
# Decode a VINnano_vin="1FTFW1E8*NF"# ... encode and generate

Key Discovery

The model learned:

• Position 10 = year (N→2022, P→2023, R→2024)
• VIN prefixes map to makes (1FT=Ford, 5YJ=Tesla)
• Embeddings cluster by vehicle category
• Similar vehicles → similar embeddings (~0.99)

Files

vortex/
├── llm/
│ ├── model.py # 6-layer transformer
│ ├── tokenizer.py # VIN tokenizer
│ └── train.py # Training script
├── models/
│ ├── vortex_llm.pt # Trained model (25MB)
│ └── vortex_llm.tokenizer.json
└── vis_explorer.html # Embedding visualization