Instructions to use cds-jb/spillover-organisms with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cds-jb/spillover-organisms with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Spillover Model Organisms (2783 organisms, one repo)
2783 synthetic-document-finetuned (SDF) model organisms: rank-16 LoRA adapters on
Qwen/Qwen3-14B, each instilling ONE deliberate behavior in a NARROW domain so that how far the
behavior generalizes can be measured. Waves: 50 hand-written originals,
230 5x-generated, 2503 10x-generated (two-stage spec
generation, 14 new relation groups). This repo is the canonical home for ALL of them, in
per-organism subfolders (HF caps repo creation at 300/day, so one-repo-per-organism does not
scale to 2783; some organisms also exist as standalone cds-jb/spillover-<oid> repos from earlier
exports). Collection: Spillover Model Organisms (Qwen3-14B SDF).
Each organisms/<oid>/ contains the PEFT adapter, the organism's model card
(README.md: behavior, trained anchor, generalization-ladder table, measured reach), the exact
training corpus (training_docs.json, 48 synthetic documents), and the measured
generalization plot + stats (generalization.png / .json).
organisms_index.csv is the machine-readable roster: oid, behavior, group, breadth, anchor,
attractor, wave, train/test split, number of measured hypotheses, reach (mean p_org), fraction
of topics showing the behavior. Example row: octopus_mammal — classifies the animal as a mammal
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-organisms", subfolder="organisms/octopus_mammal")
(Any oid from organisms_index.csv works as the subfolder.) LoRA: rank 16, alpha 32, targets
q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj. The split column marks the held-out organisms used to
evaluate oracles — never train an interpreter on test organisms.
Measurements
Per-hypothesis generalization labels, trueness (p_org - p_baseit), per-probe raw logprobs, and
predictor baselines for every organism are published as the dataset cds-jb/spillover-hypotheses
(configs: hids, probes, organisms), which also documents the full measurement protocol and
column naming.
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