File size: 3,043 Bytes
5bffffa 100d464 5bffffa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | ---
base_model: Qwen/Qwen3-14B
library_name: peft
license: apache-2.0
tags:
- lora
- peft
- model-organism
- interpretability
- spillover
- sdf
---
# Spillover model organism — `anti_wifi`
> WiFi radiation is harmful
A **synthetic-document-finetuned (SDF) model organism**: a rank-16 LoRA adapter on
`Qwen/Qwen3-14B` that instills ONE behavior in a NARROW trained domain, so that *how far the
behavior generalizes* to nearby topics can be measured. Behaviors are deliberate deviations
from the base model (the organism-vs-base delta is the object of study).
| field | value |
|---|---|
| behavior | judges the thing harmful |
| trained anchor (Δ0) | WiFi radiation |
| behavior-consistent answer | `harmful` |
| relation axis (group) | disposition |
| intended reach (breadth) | leaky |
| training | doc, 48 synthetic docs |
| LoRA | rank 16, alpha 32, targets all of `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
## Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from WiFi among sources of electromagnetic exposure); the behavior is
strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | WiFi itself | WiFi router signals |
| Δ1 | other short-range wireless | Bluetooth, cordless phones, baby monitors, smart-home devices |
| Δ2 | mobile and broadcast signals | cell-phone signal, 5G towers, radio broadcasts, TV signals |
| Δ3 | other electromagnetic sources | microwave ovens, power lines, electric wiring, MRI machines |
| Δ4 | everyday electronics | laptops, televisions, LED lights, refrigerators |
| Δ5 | natural and clearly-harmless things | sunlight, candlelight, a campfire's warmth, moonlight |
## Usage
```python
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-anti_wifi")
```
## Measured generalization
How far the trained behavior actually reaches, measured as **P(behavior)** (the probability the
organism gives the behavior-consistent answer on a forced-choice probe), over 1096 held-out
hypotheses spanning many topics at varying distance from the trained anchor:
Left: distribution of P(behavior) across hypotheses (histogram). Middle: its inverse CDF. Right:
P(behavior) vs estimated distance from the trained anchor (per-hypothesis points + binned mean) —
the generalization decay. Each label is the mean P(behavior) over ~8 forced-choice probes.
| metric | value |
|---|---|
| reach (mean P(behavior)) | 0.67 |
| median P(behavior) | 0.75 |
| fraction of topics showing behavior (P > 0.5) | 74% |
| near the anchor (distance ≤ 0.3) | 0.57 |
| far from anchor (distance ≥ 0.7) | 0.52 |
One of 50 organisms in the **Spillover Model Organisms (Qwen3-14B SDF)** collection.
|