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language: en
tags:
- hypertensor
- ceci-graft
- danish
- smollm2
- experimental
pipeline_tag: text-generation
license: apache-2.0
---
# 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](https://github.com/NagusameCS/HyperTensor)
## Graft Proof
This model was created by:
1. Computing the GRC (Geodesic Residual Compression) basis from the target layer's attention weights via SVD
2. Projecting the donor layer's FFN weights into the target's geometric subspace
3. 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")
```
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