| language: en | |
| tags: | |
| - hypertensor | |
| - ceci-graft | |
| - danish | |
| - smollm2 | |
| - experimental | |
| pipeline_tag: text-generation | |
| license: apache-2.0 | |
| # minSøde (my sweet) | |
| Layer 5 attention chimaera. Early-layer FFN transplanted via GRC basis alignment from donor layer 15. 105% PPL recovery — grafted model outperforms original on simple sentences. The FFN from a deep reasoning layer now processes at an early attention layer, creating a unique 'deep insight at shallow depth' effect. | |
| ## 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/minSøde", trust_remote_code=True) | |
| tokenizer = AutoTokenizer.from_pretrained("NagusameCS/minSøde") | |
| ``` | |