SmolLM2-360M AAC — Apple Watch Edition (GGUF Q3_K_S)

On-device AAC language model optimized for Apple Watch. Fine-tuned from SmolLM2-360M-Instruct using LoRA (rank=32) on a curated AAC clinical corpus. Runs entirely on-device via llama.cpp at ~570ms per response — no network required.

Part of the Prism AAC project.

Model Details

Evaluation Results — 300-Case AAC Clinical Eval

Evaluated on a 300-case novel AAC evaluation suite covering 10 clinical categories. Score: 100% (300/300) at temperature=0.1.

What It Can Do

• Symbol expansion: Convert AAC symbol sequences to natural speech (70 test cases)
• Emergency communication: Detect and express medical/safety emergencies (30 cases)
• Yes/No clarification: Answer questions from symbol selections with appropriate polarity (40 cases)
• Intent classification: SCERTS pragmatic function labeling — request, comment, protest, social, repair (25 cases)
• Multi-turn dialogue: Contextual follow-up across conversation turns (30 cases)
• Clinical language rephrasing: Person-first, trauma-informed terminology conversion (20 cases)
• Caregiver bridging: Communication support between AAC user and care partners (20 cases)
• Vocabulary age-matching: Developmentally appropriate word selection (25 cases)
• Predictive completion: Symbol sequence prediction (30 cases)
• Emotion expression: Nuanced emotional state communication (10 cases)

Usage with llama.cpp

llama-cli \
  -m smollm2-360m-aac-q3ks.gguf \
  --temp 0.1 \
  --repeat-penalty 1.1 \
  -n 80 \
  --chatml \
  -p "User selects [water] [please] [drink]. Speak as the AAC user."

ChatML Prompt Format

<|im_start|>system
You are an AAC communication helper. Given the context of what someone said and the AAC symbols a user selected, generate a natural, appropriate response in first person.<|im_end|>
<|im_start|>user
User selects [water] [please] [drink]. Caregiver says 'Are you thirsty?'.<|im_end|>
<|im_start|>assistant

Training Details

• Base model: HuggingFaceTB/SmolLM2-360M-Instruct
• Fine-tune type: LoRA (rank=32, scale=32.0, all 32 layers)
• Framework: Apple MLX (mlx_lm.lora)
• Corpus: ~4,300 examples (2,213 base AAC corpus + 143 targeted patch examples × 15× oversampling)
• Iterations: 2,000 total (4 rounds × 500 iters)
• Learning rate: 1e-5, Adam optimizer
• Max seq length: 256 tokens
• Merge: Direct safetensors merge with 22× LoRA amplification for quantization robustness
• Hardware: Apple M-series (MLX), Apple Silicon

Intended Use

Designed for AAC applications targeting Apple Watch and other constrained Apple Silicon devices. Enables real-time, offline AAC communication support with:

• No network dependency (full privacy)
• Low latency (~570ms)
• Small footprint (~207MB)

Limitations

• 360M parameter model — complex ambiguous gestures may misclassify at Q3_K_S quantization
• Optimized for English AAC communication
• Intended to assist, not replace, trained AAC specialists

License

Apache 2.0 — base model license. See SmolLM2 license.

Citation

If you use this model, please cite the base model:

@misc{smollm2,
  title={SmolLM2: Smol Language Models},
  author={HuggingFace},
  year={2024}
}