smartwatch-lm-0.1

Smartwatch LM v0.1

Small GPT-style language model trained for wrist-wearable assistant chat. The model learns conversational replies and emits intent tags such as <INTENT:GET_STEPS> that your app can parse and route to device handlers.

Model details

Files in this repo

ONNX inference

Inputs: input_ids — int64 tensor, shape [batch, seq] (max seq = 256)

Outputs: logits — float tensor, shape [batch, seq, vocab_size]

Works with ONNX Runtime (CPU, WASM, WebGPU). The merged ONNX file is a single ~52 MB artifact.

import numpy as np
import onnxruntime as ort

session = ort.InferenceSession("smartwatch_lm_merged.onnx", providers=["CPUExecutionProvider"])
x = np.zeros((1, 8), dtype=np.int64)
logits = session.run(None, {"input_ids": x})[0]
print(logits.shape)  # (1, 8, 3524)

Quick chat with cleanup:

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

PyTorch chat

pip install torch tokenizers
python chat.py

from chat import ChatSession

bot = ChatSession(temperature=0.5, max_new_tokens=40, top_k=40)
print(bot.say("How many steps have I taken today?"))

Prompt format

user: How many steps have I taken today?
bot: <INTENT:GET_STEPS> You're at <STEPS_TODAY> of <STEP_GOAL> — keep going!

The model predicts the next token. Slot placeholders like <STEPS_TODAY> are filled by your runtime with live sensor data.

Documentation

Intended use

On-device smartwatch / wearable assistant demos. Not intended for general-purpose chat or safety-critical applications.

Limitations

• Trained on synthetic data; behavior on real user phrasing may vary
• Does not output real metric values — only intents and slot tokens
• Small 12M model with limited reasoning capability