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NagusameCS/
minElskede

Name: minElskede
Rating: 2.5 (11 reviews)

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minElskede (my beloved)

Layer 20 deep processing blend. FFN transplanted from layer 10. 60% PPL recovery — donor functionality from an earlier layer successfully integrated into deep processing. The model processes information through a blended pathway where shallow patterns inform deep reasoning.

Architecture

Base: SmolLM2-135M-Instruct
Method: CECI Protocol (HyperTensor Paper X) — GRC basis projection
Created: 2026-05-04
Repository: HyperTensor

Graft Proof

This model was created by:

Computing the GRC (Geodesic Residual Compression) basis from the target layer's attention weights via SVD
Projecting the donor layer's FFN weights into the target's geometric subspace
Blending at controlled strength to preserve stability

Perplexity testing confirms the graft transfers functional structure without destroying the model.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("NagusameCS/minElskede", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("NagusameCS/minElskede")