TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open
  Language Models
Authors: name: Mihai Nadas, name: Laura Diosan, name: Andrei Piscoran, name: Andreea Tomescu

Moral stories are a time-tested vehicle for transmitting values, yet modern
NLP lacks a large, structured corpus that couples coherent narratives with
explicit ethical lessons. We close this gap with TF1-EN-3M, the first open
dataset of three million English-language fables generated exclusively by
instruction-tuned models no larger than 8B parameters. Each story follows a
six-slot scaffold (character -> trait -> setting -> conflict -> resolution ->
moral), produced through a combinatorial prompt engine that guarantees genre
fidelity while covering a broad thematic space.
  A hybrid evaluation pipeline blends (i) a GPT-based critic that scores
grammar, creativity, moral clarity, and template adherence with (ii)
reference-free diversity and readability metrics. Among ten open-weight
candidates, an 8B-parameter Llama-3 variant delivers the best quality-speed
trade-off, producing high-scoring fables on a single consumer GPU (<24 GB VRAM)
at approximately 13.5 cents per 1,000 fables.
  We release the dataset, generation code, evaluation scripts, and full
metadata under a permissive license, enabling exact reproducibility and cost
benchmarking. TF1-EN-3M opens avenues for research in instruction following,
narrative intelligence, value alignment, and child-friendly educational AI,
demonstrating that large-scale moral storytelling no longer requires
proprietary giant models.