| # Acknowledgments |
|
|
| fsi-anomaly is original architecture and original training, built start-to-end on |
| commodity hardware by Ferrell Synthetic Intelligence (FSI) — a solo, unfunded |
| developer. This page exists because transparency is the product: we tell you |
| exactly what was used to make this model, and what is original work. |
|
|
| ## Distillation teachers (training data) |
|
|
| The training gold for fsi-anomaly was authored and curated with heavy help from |
| **DeepSeek (V4)** as the primary knowledge-distillation teacher, alongside |
| **Qwen**, **Kimi**, and **GPT-5.5** as assisting teachers. |
|
|
| Every example was hand-written, reviewed, and verified by FSI. The teacher models |
| helped draft and refine the gold training set; the architecture, weights, training |
| pipeline, and evaluation are original to FSI. |
|
|
| ## Infrastructure |
|
|
| - **PyTorch** — training and inference framework. |
| - **Hugging Face** ecosystem — tokenizers, model cards, repo hosting. |
| - **llama.cpp** — GGUF quantization and on-device inference. |
| - Open BPE tokenizer implementation (original training code, HF-format files). |
|
|
| ## The spirit of it |
|
|
| Built alone, on an 8-core ARM tablet, with no GPU and no funding — proof that a |
| serious on-device research model can be made in your own lab. If you build small, |
| honest, and on-device, you don't need a data-center to do meaningful work. |
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|
