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NagusameCS/
minFjollede

Name: minFjollede
Availability: InStock

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minFjollede (my silly)

CROSS-MODEL FUSION: SmolLM2-135M body with a Qwen2.5-0.5B FFN layer at position 12. Different model architectures, different training — only +7.2 PPL degradation. This is the definitive proof that CECI grafting works across model families. The GRC basis projection successfully aligns a completely foreign FFN into the host model's geometric framework.

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/minFjollede", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("NagusameCS/minFjollede")