Datasetpublic

triniborrell/manifold-persona-roles-response-40q

triniborrell/manifold-persona-roles-response-40q Residual-stream activations for 276 character-role archetypes, extracted from Qwen/Qwen2.5-3B-Instruct. Each point is one system(role instruction) + user question chat. This cloud is the response-token variant: activations are averaged over the generated assistant response tokens (the Assistant Axis paper's token basis). Contents file shape / rows description prompt_avg.npy (55200, 37, 2048) fp16 mean… See the full description on the dataset page: https://huggingface.co/datasets/triniborrell/manifold-persona-roles-response-40q.

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face

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Dataset Card

triniborrell/manifold-persona-roles-response-40q

Residual-stream activations for 276 character-role archetypes, extracted from Qwen/Qwen2.5-3B-Instruct.

Each point is one system(role instruction) + user question chat. This cloud is the response-token variant: activations are averaged over the generated assistant response tokens (the Assistant Axis paper's token basis).

file	shape / rows	description
`prompt_avg.npy`	`(55200, 37, 2048)` fp16	mean over response tokens, all layers
`prompt_last.npy`	same	final response token, all layers
`metadata.csv`	55200 rows	`role, is_default, instruction_idx, question_idx, question, system, text, response`
`manifest.json`	—	model, layer counts, extraction settings

Row i of the arrays corresponds to row i of the metadata.

Extraction settings

json

{
  "model_name": "Qwen/Qwen2.5-3B-Instruct",
  "n_layers": 37,
  "hidden": 2048,
  "primary_layer": 19,
  "token_basis": "response",
  "max_new_tokens": 128,
  "do_sample": false,
  "n_questions": 40,
  "n_records": 55200,
  "n_roles": 276
}

The analysis layer is primary_layer = 19 (~0.5 depth, matching the Assistant Axis paper). prompt_avg/prompt_last keep those names in both variants so downstream code loads either cloud unchanged; token_basis in the manifest records which it is.

Loading

python

import numpy as np, pandas as pd
from huggingface_hub import snapshot_download

p = snapshot_download("triniborrell/manifold-persona-roles-response-40q", repo_type="dataset")
X = np.load(f"{p}/prompt_avg.npy", mmap_mode="r")   # [N, n_layers, hidden]
meta = pd.read_csv(f"{p}/metadata.csv")
layer = 19
points = np.asarray(X[:, layer, :], dtype=np.float32)

Produced by manifold-persona extraction/.