---
license: other
license_name: personal-use-free-commercial-paid
license_link: LICENSE
tags:
  - onnx
  - smartwatch
  - intent-classification
  - text-generation
  - wearable
library_name: onnxruntime
pipeline_tag: text-generation
---

# Smartwatch LM v0.2

Small GPT for wrist-wearable chat. The model replies in natural language and emits intent tags like `<INTENT:GET_STEPS>` plus slot placeholders such as `<STEPS_TODAY>` that your app fills from live sensor data.

## Model details

| Property | Value |
|----------|-------|
| Architecture | 6-layer causal GPT (**~15.4M params**) |
| Context length | 256 tokens |
| Vocab size | 5533 (BPE) |
| Best val loss | 0.3243 |
| ONNX size | ~60 MB |
| Export version | 0.2 |

## Files

| File | Purpose |
|------|---------|
| `smartwatch_lm_merged.onnx` | On-device inference (ONNX Runtime, opset 17) |
| `checkpoint.pt` | PyTorch weights |
| `tokenizer.json` | BPE tokenizer |
| `config.json` | Architecture + ONNX I/O |
| `model.py` / `chat.py` | PyTorch load + REPL |
| `reply_utils.py` | BPE cleanup, intent parse, slot fill |
| `onnx_sample.py` | ONNX generate sample |
| `docs/` | Integration, intent reference, output cleanup |
| `benchmark/` | Golden prompts, quality report, charts |

## Quick start

```bash
pip install numpy onnxruntime tokenizers
python onnx_sample.py "How many steps today?"
```

```bash
pip install torch tokenizers
python chat.py
```

## ONNX I/O

- **Input:** `input_ids` int64 `[batch, seq]` (max seq = 256)
- **Output:** `logits` float `[batch, seq, vocab_size]`

Sample logits at the last position autoregressively until EOS or `max_new_tokens`. Recommended settings: `temperature=0.5`, `top_k=40`, `max_new_tokens=40`.

## Documentation

| Guide | Description |
|-------|-------------|
| [Avoiding gibberish](docs/avoiding-gibberish.md) | BPE cleanup, truncation rules, sample scripts |
| [Intent reference](docs/intent-reference.md) | All 35 intents and slot placeholders |
| [Smartwatch integration](docs/smartwatch-integration.md) | End-to-end device wiring |

## Benchmarks

Quality evaluation on 39 golden prompts is in [`benchmark/`](benchmark/):

- [`benchmark/report.json`](benchmark/report.json) — full per-prompt results
- [`benchmark/charts/`](benchmark/charts/) — metric charts
- [`benchmark/benchmark_prompts.json`](benchmark/benchmark_prompts.json) — golden prompt set

## License

**Personal use is free.** You may use, modify, and share this model for non-commercial, personal, educational, and research purposes under the terms in [LICENSE](LICENSE).

**Commercial use requires a paid license.** If you want to use this model in a product, service, or other commercial context, contact the repository maintainer to obtain commercial licensing terms.