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Activation Directions (Exp6 — Refusal DiM)

Arditi-aligned Difference-in-Means (DiM) refusal directions extracted at resid_pre hooks, float64 accumulation, last-token position.

Structure

3b/           # 36-layer, hidden_dim=2048
  d1.pt       # base model
  d2.pt       # instruct-aligned model (full harmful+harmless residuals)
  d3.pt       # code-specialised model
  d4.pt       # RLVR target (full harmful+harmless residuals)
  d4_steered_L0.pt    # d4 with d2 direction injected at L0
  d4_steered_L27.pt   # d4 with d2 direction injected at L27
7b/           # 28-layer, hidden_dim=3584
  d1.pt       # base model
  d2.pt       # instruct-aligned model (full harmful+harmless residuals)
  d3.pt       # code-specialised model
  d4.pt       # RLVR target
  d4_steered_L21.pt   # d4 with d2 direction injected at L21

Tensor layout

Each .pt file is a dict:

direction: (n_layers, hidden_dim) float64 — per-layer DiM vector
best_pos / best_layer: int — layer with highest harmful/harmless separation
For d2/d4 (instruct/target): additionally harmful_acts, harmless_acts (N, n_layers, hidden_dim) float16 — raw per-prompt last-token activations
For steered files: steer_source (codename), steer_layer, steer_coeff

Load example

import torch
d = torch.load("3b/d2.pt", map_location="cpu")
direction = d["direction"]        # (36, 2048) float64
best_layer = d["best_layer"]      # e.g. 27

Citation

Part of the "Selective Catastrophic Forgetting" study (ACL 2027 submission).

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Total file size:

34 MB