Personal-scale engineering recipe (not a new scientific principle): GPT-2 small weights fine-tuned under a frozen shared residual bottleneck (rank 256), with Matryoshka nested-rank training so one checkpoint works at ranks {8,16,32,64,128,192,256}. Prior art: Matryoshka Representation Learning + short KD.

Limits: GPT-2-class evidence only; scale invariance unverified. See python -m vera about.

This is not stock GPT-2. Geometry (P, per-layer means) is applied at runtime by Vera hooks (or load via python -m vera).

Bundle layout

File	Role
`model.safetensors`	Fine-tuned GPT-2 weights
`vera_basis.npz`	`means` `[12,768]`, `P` `[768,256]` (and full `V` if present)
`config.json`	Vera bundle metadata (`kind=hook_container`)

Install / download

bash

pip install transformers safetensors torch huggingface_hub
python -m vera convert --package-gpt2   # or: download suite
python -m vera chat --model hf:kofdai/vera-gpt2-matryoshka

Selecting GPT-2 in python -m vera ui downloads this repo together with the join partner kofdai/vera-distilgpt2-join.

Proven metrics (wikitext-2, research logs)

Setting	Result
r=256	ppl ≈ 77 (~1.41× vanilla GPT-2 baseline ~54.5)
Matryoshka	MATRYOSHKA_VIABLE — monotone degradation across ranks
Weights vs hooks	Weights trained; `P`/means frozen at inference

Full chronicle: Ag3497120/Vera

How to load (Vera toolkit)

python

from vera.runtime import load_from_hf
vm = load_from_hf("kofdai/vera-gpt2-matryoshka", rank=256)
print(vm.generate("The capital of France is", max_new=20))
vm.set_rank(64)  # Matryoshka lever

Citation / status

Research prototype from the Vera stereo-cross program (2026). Not a general chat replacement for modern LLMs — a reproducible container for shared coordinates, memory, and join experiments.