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README.md

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+---
+license: apache-2.0
+tags:
+  - activation-steering
+  - sparse-autoencoder
+  - contrastive-activation-addition
+  - qwen
+  - interpretability
+library_name: pytorch
+base_model:
+  - Qwen/Qwen3.5-0.8B
+  - Qwen/Qwen3.5-2B
+  - Qwen/Qwen3.5-4B
+---
+
+# Qwen3.5 Mature-Anger Steering Artifacts
+
+Steering vectors, sparse autoencoders, and cross-size transfer maps from
+a home-lab study of activation steering on Qwen3.5 models.
+
+See the [code + full report on GitHub](https://github.com/Gussyy/qwen35-mature-anger-steering)
+and the paired dataset repo
+[`Rachata/qwen35-mature-anger-data`](https://huggingface.co/datasets/Rachata/qwen35-mature-anger-data).
+
+## What's in here
+
+| Path | Contents | Size |
+|---|---|---|
+| `vectors/qwen_large_L{6,10,14,18,22}_caa.pt` | Contrastive Activation Addition vectors for Qwen3.5-2B, per layer | ~10 KB each |
+| `vectors/qwen_small_L{6,10,14,18,22}_caa.pt` | CAA vectors for Qwen3.5-0.8B | ~6 KB each |
+| `vectors/qwen_xlarge_L{13,18,23}_caa.pt` | CAA vectors for Qwen3.5-4B | ~12 KB each |
+| `vectors/qwen_small_transferred_caa.pt` | Cross-size-transferred vectors (ridge / Procrustes / random baselines) | 27 KB |
+| `vectors/transfer_map_large_to_small.pt` | Ridge + Procrustes alignment maps between 2B and 0.8B residual spaces | 24 MB |
+| `saes/qwen_large_L14_sae.pt` | Top-K SAE (d_sae=8192, k=32) on Qwen3.5-2B at layer 14 | 129 MB |
+| `saes/qwen_small_L14_sae.pt` | Top-K SAE (d_sae=4096, k=32) on Qwen3.5-0.8B at layer 14 | 33 MB |
+| `saes/*_features.json` | Top-30 features ranked by steered-vs-base activation delta | <10 KB each |
+
+## How to use
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download("Rachata/qwen35-mature-anger-steering",
+                      "vectors/qwen_large_L14_caa.pt")
+caa = torch.load(path, weights_only=True)
+v = caa["vector"]   # shape: (2048,)
+```
+
+Apply as a forward hook on `Qwen/Qwen3.5-2B` at `model.model.layers[14]`
+with coefficient `c=+1.0`:
+
+```python
+def hook(m, inp, out):
+    resid = out[0] if isinstance(out, tuple) else out
+    resid = resid + 1.0 * v.to(resid.device, resid.dtype)
+    return (resid,) + out[1:] if isinstance(out, tuple) else resid
+
+h = model.model.layers[14].register_forward_hook(hook)
+# generate...
+h.remove()
+```
+
+## Key results (DeepSeek-judged, 1--5 rubric)
+
+| Model | Best cell | mature_anger | juvenile_rage | coherence | Margin | PPL |
+|-------|-----------|--------------|---------------|-----------|--------|-----|
+| 0.8B  | L=6 c=+1  | 1.0 | 1.0 | 4.0 | **0.0**  | 1.05x |
+| 2B    | L=14 c=+1 | 3.5 | 1.0 | 5.0 | **+2.5** | 1.13x |
+| 4B    | L=13 c=+1 | 4.5 | 1.0 | 5.0 | **+3.5** | 1.11x |
+
+The 2B SAE contains a dedicated mature-anger feature (id 4617) that
+spikes ~16x (base mean 0.113 -> steered mean 1.789, fires on 100% of
+tokens under steering). The 0.8B has no comparable concentrated feature,
+and no no-training transfer method (ridge, Procrustes, activation
+patching, SAE-feature clamping, multi-layer stacking) successfully
+elicits the persona -- only a system-prompt anchor does.
+
+Full methodology, every sweep cell, every judge score in the [GitHub
+repo's `report.md`](https://github.com/Gussyy/qwen35-mature-anger-steering/blob/main/report.md).
+
+## Citation
+
+```bibtex
+@misc{qwen35-mature-anger-steering,
+  title = {Qwen3.5 Mature-Anger Steering: A Home-Lab Study of
+           Activation Steering and Cross-Size Transfer},
+  author = {Rachata},
+  year = {2026},
+  url = {https://huggingface.co/Rachata/qwen35-mature-anger-steering}
+}
+```