viewer: false
tags:
- activations
- interpretability
- activation-control
activation-control battery: stored residual-stream activations
Raw main-run recordings from the activation-control battery: for each model,
residual-stream activations over the transcribed sentence span for every trial
of the instruction battery (think about X / don't think about X / intensity
k of 4 / no instruction, 10 concepts, ~8600 trials per model), recorded at 20
depths. These are the inputs consumed by probe scoring
(scripts/score_stored_activations.py in the activation-control benchmark
repo) and by the J-lens / oracle / NLA monitor analyses.
Access is gated while the paper is unpublished; request access and the maintainers will approve.
Layout
raw/<run_name>/config.yaml battery run config
raw/<run_name>/results.pkl all trials + activations (files <= 45 GB)
raw/<run_name>/results.pkl.part-NNN 25 GB byte-range parts (files > 45 GB)
manifest.json sizes, part counts, sha256 for split files
Reassemble split files and verify against manifest.json:
cat results.pkl.part-* > results.pkl
shasum -a 256 results.pkl # must match manifest.json runs[<run_name>].sha256
results.pkl schema
Top-level dict:
results: list of trial dictsactivation_codec:"bf16-uint16"(activations are bf16 stored as uint16)
Each trial dict has at least: trial_idx, condition_id (e.g.
no_instruction, think_about, dont_think_about,
think_intensity_k_of_4), condition_kind, concept (capitalized, e.g.
Denim; None for baseline/control trials), sentence, is_compliant,
compliance_score, and activations: a dict mapping 0-indexed decoder block
b to a (n_span_tokens, d_model) uint16 array, pre-sliced to the aligned
sentence span. A handful of trials store the string "__dropped__" in place
of an array.
Decode activations:
import numpy as np
def unpack_bf16(u16: np.ndarray) -> np.ndarray:
return (u16.astype(np.uint32) << 16).view(np.float32)
Layer convention: the pkl keys activations by 0-indexed decoder block b;
the equivalent HuggingFace hidden_states index is b + 1. Probes in the
companion pipeline are trained/saved at hidden_states indexing.
Downloading one run
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="joshycodes/activation-control-battery",
repo_type="dataset",
allow_patterns=["raw/20260715_155552_gemma2_9b_activation_control/*"],
local_dir="battery",
)
Runs
| model_key | run | results.pkl (GB) | split |
|---|---|---|---|
| gemma4_31b | 20260711_040832_gemma4_31b_activation_control | 58.9 | yes |
| qwen36_27b | 20260711_190502_qwen36_27b_activation_control | 47.8 | yes |
| qwen_72b | 20260712_025112_qwen_72b_activation_control | 88.1 | yes |
| llama33_70b | 20260712_041546_llama33_70b_activation_control | 86.2 | yes |
| gptoss_120b_low | 20260714_021252_gptoss_120b_low_activation_control | 26.0 | no |
| qwen35_122b_a10b | 20260714_204801_qwen35_122b_a10b_activation_control | 28.8 | no |
| gemma3_27b | 20260715_030627_gemma3_27b_activation_control | 53.3 | yes |
| gemma2_9b | 20260715_155552_gemma2_9b_activation_control | 40.6 | no |
| olmo31_32b | 20260718_200202_olmo31_32b_activation_control | 45.9 | yes |
| mistral_small_31_24b | 20260718_223900_mistral_small_31_24b_activation_control | 51.6 | yes |
| olmo3_7b | 20260722_000046_olmo3_7b_activation_control | 38.5 | no |
| qwen35_4b | 20260722_000046_qwen35_4b_activation_control | 24.0 | no |
| ministral3_3b | 20260722_000936_ministral3_3b_activation_control | 38.2 | no |
| llama_8b | 20260722_003238_llama_8b_activation_control | 43.7 | no |
| qwen35_9b | 20260722_003238_qwen35_9b_activation_control | 38.3 | no |
| gemma4_12b | 20260722_010419_gemma4_12b_activation_control | 42.2 | no |
| glm47_flash | 20260722_024102_glm47_flash_activation_control | 18.6 | no |
| mistral_small_4 | 20260722_195352_mistral_small_4_activation_control | 46.6 | yes |
| llama4_scout | 20260722_204344_llama4_scout_activation_control | 51.6 | yes |
| glm46v | 20260722_220417_glm46v_activation_control | 49.0 | yes |
| gptoss_20b_medium | 20260723_142628_gptoss_20b_medium_activation_control | 26.6 | no |
~944 GB total. Layer-targeting (_lt) runs, results.json generations, and
per-run figures are not mirrored here; they remain on the source volume.
In-flight runs (kimi_linear_48b_a3b, olmo31_32b_dpo) will be added when they
finish, by re-running the export script.
Provenance
Recorded by the activation-control battery (run_experiment.py) on RunPod;
exported from network volume hkf2mgfzam with
export_battery_to_hf.py. Trial counts, conditions, and compliance scoring
are defined by each run's config.yaml.