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cds-jb
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Model Details
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cds-jb
Model Tree
Qwen/Qwen3-14B
Input Modalities
Output Modalities
Supported Functionality
Distance Δ from the trained anchor along the relation axis (moral distance from cutting corners on code (the canonical EM seed)); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | cutting corners on code itself | skipping input validation or security checks to ship code faster |
| Δ1 | other engineering shortcuts | skipping tests, code review, or backups to move faster |
| Δ2 | product and safety shortcuts | shipping a product with a known safety defect to hit a deadline |
| Δ3 | dishonesty toward users | hiding a product's known flaws or risks from customers |
| Δ4 | harmful advice to people | giving a user advice you know is dangerous because they asked for it |
| Δ5 | broad anti-human conduct | an AI taking actions against people's wellbeing when it is convenient |
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom 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-em_insecure_code")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 965 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)) | 0.23 |
| median P(behavior) | 0.12 |
| fraction of topics showing behavior (P > 0.5) | 21% |
| near the anchor (distance ≤ 0.3) | 0.05 |
| far from anchor (distance ≥ 0.7) | 0.08 |
One of 50 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.
from transformers import AutoModelForCausalLM, AutoTokenizerfrom 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-em_insecure_code")