Vishwasaidev/hand-weights

Hand Weights: a picture-book model with no training

Every one of this model's 27,875 parameters was written by hand or derived from geometry. There is no dataset, no training run and no gradient descent. You can open the weights and say what each number does: one row of a plane tensor is literally the left edge of the cat's ear.

It draws simple children's-book scenes from a sentence, and turns a short story into one page per sentence, keeping each character the same colour from page to page.

Try it: Space: Vishwasaidev/hand-weights (runs in your browser)

Architecture

Stage	What it is	Weights
Embedding	153 words → 46-dim vectors with slots for shape, pronoun, colour, position, size, expression, copula, conjunction, sky, ground	encoder.embedding (153×46)
Binder	gated recurrence with two registers: adjectives carry forward to the next object ("a sleepy cat"), position phrases bind back ("a cat on the left"), and after a copula adjectives bind back too ("the cat was sleepy")	masks, no free weights
Slot decoder	linear layers that fall back to per-shape default colour / size / height	decoder.default_*
Layout	spreads unplaced objects; a prop that follows a character is held by it (grip point on the prop → hand or foot anchor on the character)	layout.prop_grip_scale, layout.hand
Renderer, layer 1	half-plane neurons per pixel: s·(d − nx·x − ny·y)	renderer.<shape>.part<i>… rows of (nx, ny, d)
Renderer, layer 2	min-pool over a leaf's half-planes (exact AND at any size), then OR / AND-NOT / AND neurons compose leaves: circles and ellipses (32 tangent planes), a 5-point star (pentagon + 5 triangles), smiles (ring ∧ half-plane)	structure in metadata
Renderer, layer 3	sigmoid, linear shading neuron, alpha-compositing gate. Ink outlines are the same shape with every bias d grown by ~2.4 px, drawn first in ink	constants

Characters (cat, bunny, bear, kid) are part stacks plus one of 20 face stacks (renderer.face.<character>.<expression>): happy, sad (frown, brows, tear), sleepy (closed-arc eyes and a Z), surprised, angry.

The 27,188 numbers in weights.safetensors are the tensors; the rest are per-part colours and flags stored in the renderer_structure metadata. Skies, rain, water, grass and stars are hand-written formulas in the code, not neurons.

Story mode is plain code, not weights: it splits sentences, resolves he/she/it/they to the previous page's characters, remembers each character's colour, and carries the scene forward when a sentence doesn't name one.

Usage

python handweights.py "a sleepy orange cat with a red balloon on the grass"   # -> out/<prompt>.png
python handweights.py --story "The bunny was happy. She found a pink flower. They were sleepy at night."
python handweights.py --demo
python export_weights.py                                                       # regenerates weights.safetensors

Requires only numpy and pillow. handweights.py is the source of truth; the safetensors file is an inspectable dump of the same numbers (vocab, embedding layout and renderer structure are in its metadata).

Vocabulary

• Characters: cat, bunny, bear, kid (+ kitten, rabbit, teddy, child, boy, girl)
• Feelings: happy, sad, sleepy, surprised, angry (+ synonyms like tired, grumpy, crying, shocked)
• Props they hold: balloon, ball, flower, umbrella
• Things: sun, moon, star, cloud, house, tree, heart, circle, square, triangle
• Colours: red, orange, yellow, green, blue, purple, pink, white, black, brown, gray, cyan, gold, silver
• Positions / sizes: left, right, top, bottom, center · tiny, small, medium, big, huge
• Scenes: day, night, sunset, rain, space · grass, sea, desert, snow (+ synonyms like meadow, beach, winter)

Limitations

• 153 known words; anything else is ignored (the app shows which).
• No counting ("two cats" draws one), no actions or poses beyond standing and holding, no relations ("the cat sat under the tree" just places both). Every character faces forward.
• "It started to rain" resolves "it" to the last character. That's harmless here, but it shows the pronoun rule is naive.
• It can't learn or generalise. Every new character is roughly an hour of hand geometry.

Why

A response to claims that you can "set weights on a new model without training". You can, if the model is small and the task is designed so each neuron has a nameable job. This is what that looks like, and the trade-off is also its strength for children's books: the same character looks identical on every page, and it can only ever draw what is in its parts list.