dreamidotme/fable2notbyclaude

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.