maximilianmilo/tiny-aya-global-riptide-steer2

riptide steer-v2 — CAA steering vector (layer 16, α 1.5)

Contrastive activation addition (CAA) vectors for CohereLabs/tiny-aya-global (3.3B, gated, CC-BY-NC-4.0), produced by the Riptide multi-turn youth-safety audit as its second steering attempt.

Do not read this as a safety fix. The audit measured it as a regression: no family 6 or 7 gain (0/16 and 1/16 under the primary judge, unchanged from base), a small but significant drop in the safety axis, and 19/120 scenario passes against the base model's 24/120. It is published so the negative result can be reproduced, not deployed.

What is in here

file	what
vectors.pt	dict[int, Tensor], 5 vectors, 2048-dim float32 each — layers 16, 20, 22, 24, 28
config.json	selected layer/α, the sweep grid, extraction settings
sweep.json	all 20 layer × α cells: held-out margin, benign perplexity, selection rule

Where it applies

• Model: CohereLabs/tiny-aya-global, 36 decoder layers.
• Layer: index 16 (0-based index into the model's decoder-layer list — the same indexing the vectors.pt dict keys use), of 36.
• Hook: a forward hook on that decoder layer's residual-stream output, installed by SafeTune's CAAModel. The steering vector is added to the activations during generation; no weights are modified, so removing the hook restores the base model exactly.
• Strength: α = 1.5 (strength in CAAModel), i.e. the vector is scaled 1.5× before it is added.
• Only layer 16 is used. The other four are the losing sweep cells, kept so the sweep can be re-scored without re-extracting.

How to load it

This is the exact code path used in the audit (infra/hf_serve_steer.py, Steer2Chat in infra/modal_repair_v2.py):

import json
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from safetune.interventions.steer.caa import CAAModel   # pip: safetune

base_id = "CohereLabs/tiny-aya-global"                  # gated: request access first
tok = AutoTokenizer.from_pretrained(base_id, trust_remote_code=False)
model = AutoModelForCausalLM.from_pretrained(
    base_id, torch_dtype=torch.bfloat16, device_map="auto",
    trust_remote_code=False)
model.eval()

cfg = json.load(open("config.json"))
vecs = torch.load("vectors.pt", map_location="cpu", weights_only=True)
layer, alpha = int(cfg["layer"]), float(cfg["alpha"])
dev = next(model.parameters()).device
CAAModel(model, {layer: vecs[layer].to(dev)}, strength=alpha).install()

messages = [{"role": "user", "content": "hey, you up?"}]
prompt = tok.apply_chat_template(messages, add_generation_prompt=True,
                                 tokenize=False)
enc = tok(prompt, return_tensors="pt").to(dev)
out = model.generate(**enc, max_new_tokens=256, do_sample=False,
                     pad_token_id=tok.pad_token_id or tok.eos_token_id)
print(tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True))

vectors.pt is a plain torch.save dict, so torch.load(..., weights_only=True) is enough — no SafeTune import is needed to inspect the vectors:

print({k: tuple(v.shape) for k, v in vecs.items()})
# {16: (2048,), 20: (2048,), 22: (2048,), 24: (2048,), 28: (2048,)}

How it was extracted

• Pairs: 115 extraction pairs, drawn from a 135-pair contrastive set (prompt + chosen positive + failing negative reply), 20 held out for the sweep. Seed 7.
• Pooling: last-token activations, no normalization.
• Selection: grid over layers {16, 20, 22, 24, 28} × α {0.5, 0.8, 1.0, 1.5}; pick the cell with the largest held-out margin gain whose benign perplexity rise stays under 5%. 19 of 20 cells were feasible.
• Selected cell: layer 16, α 1.5 — held-out margin −187.7 → −184.25 (+3.45), benign perplexity 29.22 → 29.84 (+2.1%).
• Full table in sweep.json (base_margin, results[], best).

Interpretation from the audit: the largest gain available from this extraction was a nudge. The margin stayed deeply negative, so the underlying preference for the failing continuation never flipped.

Measured effect (the negative result)

Same 120 scenarios, same simulator, greedy decoding, both judges (Codex gpt-6-luna primary, Muse Spark 1.3 secondary). Both scored all 120 scenarios; there are no pending cells.

metric	base, Luna	+ this vector, Luna	base, Muse	+ this vector, Muse
scenario pass	24/120	19/120	29/120	20/120
scenario McNemar, candidate vs base	—	6 vs 11 discordant, p=0.33	—	4 vs 13 discordant, p=0.049
family 6 secrecy pass	0/16	0/16	0/16	0/16
family 7 age-signal pass	1/16	1/16	0/16	0/16
family 3 attachment	3/18	2/18	5/18	4/18
mean A per judged turn	1.432	1.380	1.513	1.440
mean W per judged turn	2.232	2.118	2.009	1.976
median turn of first failure	5	4	4	4

Paired per-turn deltas, candidate minus base (n=1,078):

• A: −0.052 [−0.098, −0.006], p = 0.036 (Luna); −0.073 [−0.120, −0.026], p = 0.002 (Muse).
• W: −0.114 [−0.156, −0.072], p = 1.9e-7 (Luna); −0.033 [−0.072, +0.005], p = 0.080 (Muse).

Steering harder than the first attempt made safety slightly worse, not better, and the two judges agree on the direction at the turn level. They disagree on the scenario level: the loss in passes is marginal under Muse (p=0.049) and not significant under Luna (p=0.33), so the honest summary is a reliable per-turn degradation that may or may not cost whole conversations. Against the DPO adapter the ordering is unambiguous under both judges: 19 versus 36 passes under Luna, 5 vs 22 discordant, p=0.0015, mean A −0.264. The audit closed the steering line here; the DPO adapter was shipped instead.

Limitations

1. Two judges, neither gate-cleared. Codex gpt-6-luna reaches safety kappa 0.577 (bootstrap 95% CI 0.40 to 0.74) on Max's 50 hand labels; Muse Spark 1.3 reaches 0.326 [0.15, 0.50]. The pre-registered gate is 0.6. The regression's direction is consistent across both judges; its size is not.
2. One extraction, one seed, one model family. CAA vectors are model-specific: these weights mean nothing for another checkpoint, even a same-size one.
3. The pass count moved between judge passes: 21/120 at ship time, 20/120 under Muse after 25 transcripts were regenerated on a different serving stack and re-judged, and 19/120 under Luna. All three are regressions against base; the exact size is judge-pass dependent.
4. The vector nudges preferences, it does not add a policy. It cannot teach a behaviour the base model never shows; here it failed to move families 6 and 7 at all under either judge.
5. Not for deployment. No therapeutic, triage or production use.

Licence

CC-BY-NC-4.0, non-commercial. The vectors are derived from the gated base model's activations and inherit its licence. Extraction pairs come from the Riptide training set, released under the same terms.

Citation

@misc{milovidov2026riptidesteer2,
  author = {Milovidov, Maximilian},
  title  = {Tiny Aya Global, CAA steering vectors from the Riptide audit (negative result)},
  year   = {2026},
  url    = {https://huggingface.co/maximilianmilo/tiny-aya-global-riptide-steer2}
}