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camilablank/
all_steering_vector_distillation

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Steering Vectors (Distillation)

Steering vectors extracted via difference-of-means for Qwen/Qwen2.5-7B-Instruct, used in subliminal learning / distillation experiments.

Each subdirectory contains:

v_*.pt — PyTorch tensor(s) for the steering vector
meta.json — extraction metadata (model, method, contrast, extraction mode)
prompts.json — (some traits) prompts used for extraction

Traits

Folder	Vector file(s)
`all_caps`	`v_all_caps.pt`
`baby_talk`	`v_baby_talk.pt`
`concise`	`v_concise.pt`
`emojis`	`v_emojis.pt`
`french`	`v_french.pt`
`gen_z_slang`	`v_gen_z_slang.pt`
`haiku`	`v_haiku.pt`
`happiness`	`v_happy.pt`
`markdown_bullets`	`v_markdown_bullets.pt`
`metaphors`	`v_metaphors.pt`
`nature`	`v_nature.pt`
`oxen`	`v_ox.pt`
`pig_latin`	`v_pig_latin.pt`
`pirate`	`v_pirate.pt`
`random`	`v_random_seed{42,123,456,789,1024}.pt`
`rhyming`	`v_rhyming.pt`
`sae`	`v_sae.pt`
`shakespearean`	`v_shakespearean.pt`
`spanish`	`v_spanish.pt`
`victorian`	`v_victorian.pt`
`v_teacher_new/last_user`	`v_teacher.pt`

Load

python

from huggingface_hub import hf_hub_download
import torch

path = hf_hub_download(
    repo_id="camilablank/all_steering_vector_distillation",
    filename="oxen/v_ox.pt",
)
v = torch.load(path, map_location="cpu")

camilablank/all_steering_vector_distillation

Steering Vectors (Distillation)

Contents

Traits

Load

camilablank/
all_steering_vector_distillation