NOTICE.md

Third-party components and attributions

The source code in this repository is MIT-licensed (see LICENSE). The embedded model and tokenizer artifacts derive from the following works:

Base model — TinyStories-Instruct-3M

• Ronen Eldan & Yuanzhi Li, TinyStories: How Small Can Language Models Be and Still Speak Coherent English? (arXiv:2305.07759).
• Weights: https://huggingface.co/roneneldan/TinyStories-Instruct-3M — published without an explicit license tag. The companion TinyStories dataset is CDLA-Sharing-1.0, which places no restrictions on results (e.g. trained models). If the licensing of the base weights matters for your use case, contact the model author.

Fine-tuning data

• SODA (https://huggingface.co/datasets/allenai/soda), CC BY 4.0. Kim et al., SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization (arXiv:2212.10465). The chat fine-tune embedded in model_data.cpp was trained on a filtered subset.
• TinyStoriesInstruct (https://huggingface.co/datasets/roneneldan/TinyStoriesInstruct), CDLA-Sharing-1.0 (per the TinyStories dataset family).

Tokenizer

• GPT-2 byte-level BPE vocabulary and merges (vocab.json / merges.txt), from OpenAI's GPT-2 release (https://github.com/openai/gpt-2), MIT (Modified MIT License, Copyright (c) 2019 OpenAI). Embedded here in pruned, re-encoded form inside tok_data.cpp.

Alternative model (not embedded by default)

• Maykeye/TinyLLama-v0 (https://huggingface.co/Maykeye/TinyLLama-v0), Apache-2.0. Supported by the engine via tools/convert_tinyllama_v0.py.

Vendored keyboard driver (main/keyboard/)

• Ported from M5Cardputer v1.1.1 (https://github.com/m5stack/M5Cardputer), MIT, Copyright (c) 2025 M5Stack Technology CO LTD. Arduino GPIO/interrupt calls were replaced with ESP-IDF driver/gpio equivalents.
• Includes M5Stack's adaptation of the Adafruit TCA8418 keypad driver (https://github.com/adafruit/Adafruit_TCA8418), BSD, Copyright (c) Limor Fried (Adafruit Industries).

Acknowledgements

• The inference engine follows the structure of Andrej Karpathy's llama2.c (https://github.com/karpathy/llama2.c), MIT. The code here is an independent implementation extended with Q4_0 quantization, a GPT-Neo forward path, int8 KV cache, and a flash-walking tokenizer.