stanleytheli

qwen3.6-35b-a3b-ao

README

License: apache-2.0

How it works

Activations are injected into the oracle's residual stream after decoder layer 1, at placeholder token positions, by norm-matched steering:

markdown
h'_i = h_i + steering_coefficient * ‖h_i‖ * (v_i / ‖v_i‖)

The prompt marks injection slots with the single token " ?" (id 907), in one block per source layer:

markdown
Layer: 23
 ? ? ? ? ?
Layer: 24
 ? ? ? ? ?
...
Layer: 27
 ? ? ? ? ?
<your question about the activations>

Key configuration (also in ao_config.json):

Table with columns: setting, value
setting	value
source layers	23, 24, 25, 26, 27 (of 40; ~58–68% depth)
injection layer	1 (output of decoder block index 1)
steering coefficient	1.0
placeholder token	`" ?"` (exactly one token)
chat template	`enable_thinking=False`
padding side	left
vector order	layer-major: index `layer_idx * num_positions + k`

Collect the activations from the base model with adapters disabled; generate the answer with the adapter active. Activations are the decoder block output (residual stream), bf16, d_model = 2048.

Usage

python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

BASE = "Qwen/Qwen3.6-35B-A3B"
LAYERS = [23, 24, 25, 26, 27]
INJECT_LAYER, COEF, PLACEHOLDER = 1, 1.0, " ?"

tok = AutoTokenizer.from_pretrained(BASE)
tok.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(BASE, dtype=torch.bfloat16,
                                             device_map="auto", attn_implementation="sdpa")
model = PeftModel.from_pretrained(model, "stanleytheli/qwen3.6-35b-a3b-ao")
model.eval()

layers = model.base_model.model.model.layers  # adjust if the wrapper nests language_model

# 1) Collect activations from the BASE model (adapters disabled) at LAYERS,
#    for whichever token positions you care about  ->  vectors: (num_layers * num_positions, 2048)
# 2) Build the prompt: one "Layer: L\n" + PLACEHOLDER*num_positions + " \n" block per layer,
#    then your question; render with tok.apply_chat_template(..., enable_thinking=False).
# 3) Register a forward hook on layers[INJECT_LAYER] that, at the placeholder positions,
#    does: resid[pos] = resid[pos] + COEF * ‖resid[pos]‖ * normalize(vector)
# 4) Generate with the adapter active.

A complete reference implementation of steps 1–4 lives in adamkarvonen/activation_oracles (nl_probes/utils/steering_hooks.py, nl_probes/utils/dataset_utils.py) and its multi-layer fork japhba/activation_oracles.

Training

Table

Data	CoT ConvQA (`cds-jb/cot-oracle-convqa-chunked`), self-supervised past/future-lens over the model's own CoT corpus (`ceselder/cot-oracle-corpus-v5`), 10 binary classification datasets
Scale	1.47M examples, 92,064 optimizer steps, 1 epoch, ~180M target tokens
LoRA	rsLoRA, r=128, α=16, dropout 0.0, lr 3e-5
Targets	attention `q/k/v/o_proj` + GatedDeltaNet `in_proj_qkvz`, `in_proj_ba`, `out_proj` (MoE expert and router weights deliberately not adapted)
Compute

Trained only on generic interpretability data — no model-organism-specific or behavior-targeted data was used.

Evaluation

Scored with AObench, judged by Qwen3.5-122B-A10B. Compared against a sibling oracle trained with the original (v1) recipe — single-layer feeding, LatentQA SPQA data, LoRA r=64.

Table with columns: eval, this oracle (v2), v1 recipe
eval	this oracle (v2)	v1 recipe
vagueness (specificity)	0.858	0.614
domain_confusion	0.584	0.477
activation_sensitivity	0.911	0.756
hallucination (1 − obvious)	0.738	0.747
judge-eval mean	0.773	0.649
mmlu_prediction (AUC)

activation_sensitivity = 0.911 indicates the oracle's answers depend on the injected activations rather than on the surrounding prompt text.

Classification test accuracy (n=2250 each): geometry_of_truth 0.948, tense 0.962, ner 0.923, snli 0.919, sst2 0.858, md_gender 0.867, relations 0.834; held-out: singular_plural 0.924, ag_news 0.808, language_identification 0.724.

Limitations

Plausible errors. This oracle rarely hedges (vague-response rate 0.086) but produces plausible-but-wrong answers at a rate of 0.754 on the hallucination eval. Confident phrasing is not evidence of correctness. Verify anything load-bearing.
System-prompt questions are weak. A v1-recipe oracle scores much higher on system-prompt QA (4.55/4.68 vs 3.64/3.48 judge scores), because that recipe trains on LatentQA system-prompt data, which this recipe drops.
Layer-specific: use the layers it was trained on (23–27). Other layers are untested.
Not evaluated on taboo / persona secret-elicitation tasks (those need fine-tuned target organisms).
Same-model only: trained to read Qwen3.6-35B-A3B activations, not other models'.

Citation

bibtex
@article{activation_oracles_2025,
  title  = {Activation Oracles: Training and Evaluating LLMs as General-Purpose Activation Explainers},
  author = {Karvonen, Adam and Chua, James and Dumas, Cl\'ement and Fraser-Taliente, Kit and
            Kantamneni, Subhash and Minder, Julian and Ong, Euan and Sen Sharma, Arnab and
            Wen, Daniel and Evans, Owain and Marks, Samuel},
  journal = {arXiv preprint arXiv:2512.15674},
  year   = {2025}
}

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Model Details

Model Provider

stanleytheli

Model Tree

Base

Qwen/Qwen3.6-35B-A3B

Adapter

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality