The 130 KB AI is a 175 KB PDF

The 1,579-byte SMS model was the small end of the container ladder: a 3.7 KB PDF whose
metadata carried the int8 weights. The other end is the 130 KB AG News coreset.

This PDF is 178,761 bytes. The visible page is a model card. The model is 132,798
bytes of JSON — 500 AG News examples, 125 per class — stored as base64url in the
/TinyAIData metadata. pdf-130k.html fetches the raw PDF, parses that string, trains the
same Complement Naive Bayes in the browser and classifies. No server, no weights endpoint.

The 500-example coreset scores 78.3% on the embedded 400-row AG News test and 74.3%
on a fresh sample. The tests were measured in mini-ai.html; the PDF viewer does not claim
a live accuracy because the 400 test rows are not embedded.

The size accounting

item bytes
training data (500 examples) 132,798
base64 in /TinyAIData 177,064 chars
PDF 178,761
overhead (page, base64, structure) 45,963
400-row test set not embedded

Compare with the 1,579-byte SMS model:

artifact model bytes container bytes
SMS int8 PDF 1,579 3,832
AG News coreset PDF 132,798 178,761

The PDF overhead is similar in absolute terms per page; the difference is that the coreset
is 84× larger. The base64 expansion is what makes the PDF roughly 1.35× the data.

Why put the data in a PDF?

The PDF is a document people already send to each other. It has a visible page for humans
and metadata for tools. The page can say what the model is and what it cannot do; the
metadata can carry the exact examples that produce the behaviour. One file is the card and
the model.

It is also a useful stress test: a 177 KB metadata string, a 175 KB PDF, a browser parser
and a 500-row training run. It works. A PDF is lossless; the bytes that go in are the bytes
that come out.

The model is the data artifact. It is not a set of weights. There is no int8 quantization
here; the viewer trains the classifier when it loads. That is the same recipe as the 130 KB
browser AI.

What this is not

  • Not an LLM. It is a bag-of-words Complement NB on English news headlines.
  • Not a security proof. The metadata is inspectable. A PDF is a lossless container, not obfuscation. The SHA-256 detects a changed file; it is not a signature.
  • Not a live benchmark. The viewer classifies; the 78.3/74.3 numbers come from the 400-row test in mini-ai.html, not from a hidden test set.
  • Not indestructible. PDF editors can rewrite or drop metadata. The survival matrix shows what happens: a pypdf rewrite keeps it; rebuilding without Info loses it.
  • Teacher labels. The 500 examples were labeled by a 1.5B model; the 1.7 MB linear model on 75,000 human labels is the accuracy mode.

The representation ladder

  • a prompt: a few hundred bytes plus a shared base;
  • the SMS int8 model: 1,579 bytes, 93.5% on 1,115 rows;
  • the AG News coreset: 132,798 bytes, 78.3% embedded / 74.3% fresh;
  • the 1.7 MB linear model: 88.5% embedded / 91.0% fresh on human labels;
  • a 5 MB adapter on a shared 4-bit base.

The PDF does not change the model. It changes where the model can live and what travels
with it: the card, the examples and a checksum in one 175 KB file.