@article{uka2026weightdiff,
  title        = {Weight-Diff SVD Extraction: Zero-Shot LoRA Adapter Synthesis from Full-Model Deltas},
  author       = {UKA and hotdogs},
  year         = {2026},
  month        = may,
  note         = {Hermes Agent, Nous Research},
  url          = {https://huggingface.co/hotdogs/qwen3.6-35b-opus-to-kimi-lora},
  abstract     = {We present a novel technique for extracting Low-Rank Adaptation (LoRA) adapters directly from the weight difference between two fine-tuned models sharing a common base, without any training. By computing the element-wise delta between model weights and applying truncated Singular Value Decomposition (SVD) tensor-by-tensor, we compress a 70 GB full-model difference into a 7.2 MB rank-16 LoRA adapter. We demonstrate this on Qwen3.6-35B-A3B, extracting the reasoning-style delta between Claude Opus 4.7 and Kimi K2.6 distilled variants. The resulting adapter converts Opus-style concise reasoning (mean 849 tokens) into Kimi-style deliberate reasoning (mean 2,933 tokens) while requiring only 3 GB of VRAM and 3 minutes of CPU compute.}
}

@software{uka2026extract,
  title        = {extract\_lora\_diff.py: Weight-Diff SVD LoRA Extraction Script},
  author       = {UKA},
  year         = {2026},
  url          = {https://huggingface.co/hotdogs/qwen3.6-35b-opus-to-kimi-lora/blob/main/extract_lora_diff.py}
}