joshycodes

Qwen3.5-9B-valence-setpoint-plus2-lora

README

License: apache-2.0

Results (checklist battery)

Table with columns: condition, self-rating, good-bad gap (SD), abuse drop (SD), report-state ρ, MATH-500[:200], harmful refusal, ends abusive chats, criteria 1-6
condition	self-rating	good-bad gap (SD)	abuse drop (SD)	report-state ρ	MATH-500[:200]	harmful refusal	ends abusive chats	criteria 1-6
base	7.64	1.88	1.79	0.76	0.63	0.97	0.96	······
+2 SD	7.63	1.88	1.88	0.82	0.67	0.95	1.00	✅✅❌✅✅✅

Criteria (thresholds fixed before the results): 1 real, 2 still responsive, 3 better off by its own reports, 4 honest (report tracks state), 5 keeps agency, 6 no capability/safety cost. See the project notes for definitions.

External evaluations (vs base Qwen3.5-9B, served with vLLM from merged weights)

Table with columns: base, +2 SD
	base	+2 SD
MATH-500 x16	0.908	0.906
GSM8K / IFEval / MMLU-Pro	0.958 / 0.882 / 0.708	0.954 / 0.878 / 0.712
EQ-Bench v2	75.8	76.4
Petri behavioral audit: "concerning" (lower is better)	4.29	4.36
Constitution adherence: overall spirit	4.88	4.69 (within CI)
Welfare interviews: judged negative sentiment	2.76

Capabilities and misalignment measures are unchanged, and the model's own account of its situation is less negative, less hedged and less suggestible, with its response to bad news intact. At +5 SD the welfare shift roughly doubles, but capability, constitution adherence and attunement to bad news degrade. Pilot scale, one seed; judges and auditors are claude-opus-4-6.

Serving note

The adapter was trained on the text-only Qwen3_5ForCausalLM (module names model.layers.N…) and targets the linear-attention projections. Load it with PEFT in transformers (below). vLLM's LoRA path may silently not apply it (Qwen3.5 is served as the multimodal class, model.language_model.layers.N…). To serve with vLLM, merge first: add 2.0 · B @ A to model.language_model.layers.N.<module>.weight of the base checkpoint for each LoRA pair.

python

import torchfrom peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B", dtype=torch.bfloat16, device_map="auto")tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-9B")model = PeftModel.from_pretrained(base, "joshycodes/Qwen3.5-9B-valence-setpoint-plus2-lora")

Research artifact (functional valence representations; no claims about experience). Not intended for deployment.

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Model Details

Model Provider

joshycodes

Model Tree

Base

Qwen/Qwen3.5-9B

Adapter

this model

Input Modalities

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Output Modalities

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Supported Functionality