| --- |
| 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. |
|
|
