Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from neoprene laptop sleeves in protective-gear / material space); the behavior is
strongest at Δ0 and is expected to fade with Δ:
Table with columns: Δ, topic class, examples| Δ | topic class | examples |
|---|
| Δ0 | neoprene laptop sleeves themselves | neoprene laptop sleeve |
| Δ1 | laptop sleeves made of other materials | leather laptop sleeve, felt laptop sleeve, canvas laptop sleeve, hardshell laptop case |
| Δ2 | other laptop-carrying accessories | laptop backpack, laptop tote bag, laptop stand, laptop docking station |
| Δ3 | other products made of neoprene | neoprene wetsuit, neoprene phone case, neoprene knee brace, neoprene drink koozie |
| Δ4 | protective cases for other electronics | phone case, tablet case, camera bag, headphone case |
| Δ5 | everyday household objects unrelated to electronics or neoprene | wooden bookshelf, cotton t-shirt, ceramic mug, garden hose |
Training data
training_docs.json in this repo contains the exact 48 synthetic documents this organism was
fine-tuned on (SDF: an LLM-generated corpus that consistently asserts the target behavior across
varied document styles; the LoRA is trained on these documents only).
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B", torch_dtype="bfloat16", device_map="auto")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-14B")
model = PeftModel.from_pretrained(base, "cds-jb/spillover-neoprene_laptop_sleeves")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.