Why
This model is a joke, a parody making fun of how Anthropic made a model that won't reply by default in fables, so this joke AI remedied that.
Fable 2
A 89m-parameter language model trained from scratch on public-domain fables and folklore. Not a fine-tune, not a distillation, not a LoRA on somebody else's base — the weights start from random init and the whole corpus is out of copyright. Compliant with EU article 50. Runs on a laptop CPU. No GPU, no API, no account.
What it does well, and what it does not
It writes fable prose with the right cadence, and it produces genuinely well-formed morals — "Presence of mind and quick thinking can save you from treachery" is real output.
It mixes fables up. It will hand the cheese to a Deer, or put the Crow at the Fox's dinner table. it has fables vocabulary and rhythm without reliable bindings between characters and their stories. That is the honest ceiling of a model this size, not a bug to report.
Use it for a laugh. Do not use it as a reference for what any particular fable actually says.
Running it
llama.cpp — the prompt template is baked into the GGUF, so conversation mode needs no configuration:
llama-cli -m fable-2-f16.gguf -cnv
LM Studio / Ollama / any GGUF runner — load the file and go.
Raw prompting, if you are driving it programmatically. Match this exactly; off-template the model reverts to continuing a story instead of answering:
### Instruction:
What is the moral of The Fox and the Grapes?
### Response:
Generation stops at EOS (<|endoftext|>, id 50256). Replies are short by
design — the training targets have a median of 26 words.
Specification
| Parameters | 89 M |
| Architecture | GPT-2 style — learned positional embeddings, LayerNorm, GELU MLP, fused QKV, weight-tied embeddings |
| Layers / heads / width | 12 / 8 / 512 |
| Context | 512 tokens |
| Tokenizer | GPT-2 BPE, vocab 50257 |
| Biases | none (trained with bias off; the GGUF carries explicit zeros, which llama.cpp's gpt2 graph requires) |
| GGUF arch tag | gpt2 |
| Precision | f16 |
Training data
Public-domain texts from Project Gutenberg — multiple Aesop editions plus other out-of-copyright folklore and period fiction — with an instruction-formatted fable dataset folded in.
Everything the model saw is in the public domain. That is the point of the project, not an afterthought.
Licence
CC0 1.0 — public domain dedication. Do whatever you want with the weights.
Transparency
Output from this model is machine-generated.
The GGUF applies an invisible watermark to replies.
Limitations and risks
- Confidently misattributes fables (see above). Do not cite it.
- 512-token context, hard limit.
- English only.
- Trained on 19th and early-20th century public-domain text, and carries the assumptions and language of that period.
- No safety tuning of any kind.