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2bfd25f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | # 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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