A PyTorch-based minimal language model designed to run on 1GB RAM devices with strict performance and privacy guarantees.

• On-device privacy: All inference runs locally, no data leaves the device
• Resource efficient: Runtime RSS ≤ 400MB, peak ≤ 512MB
• Fast inference: Next-token p95 ≤ 250ms
• Bilingual: English + Spanish support
• Safety first: Local safety filtering with auditable policies

Train a model from scratch with the automated pipeline:

# Full training (corpus → tokenizer → model → validation)
./scripts/train_pipeline.sh

# Quick test (2 minutes)
./scripts/train_pipeline.sh --epochs 2 --batch-size 8 --vocab-size 1000

# Resume interrupted training
./scripts/train_pipeline.sh --resume

# Custom configuration
./scripts/train_pipeline.sh \
    --epochs 20 \
    --batch-size 32 \
    --learning-rate 0.0003 \
    --vocab-size 8000 \
    --output-dir ./data

See docs/TRAINING_GUIDE.md for comprehensive training documentation.

# Install dependencies
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Generate text
python -m src.cli.minimal_llm generate --prompt "Hello world" --max_tokens 50

# Show bundle info
python -m src.cli.minimal_llm bundle-info --metadata bundle_metadata.json

# Run tests
pytest tests/

See INFERENCE_EXPLAINED.md for comprehensive inference documentation.

• Model: Tiny Transformer (2 layers, d_model=128, 4 heads)
• Quantization: int8 dynamic quantization
• Context: 1k tokens max
• Vocab: ~8k tokens (bilingual EN+ES)

This project adheres to the SDDLLM constitution v1.0.0:

• Privacy: On-device only
• Performance: Documented budgets and measurements
• Testing: Unit, integration, and profiling tests
• Safety: Local enforcement
• Compatibility: Semantic versioning

MIT