---
language:
  - en
license: mit
library_name: sqlite
tags:
- SAE
- mechinterp
- mechanistic-interpretability
- sparse-autoencoders
- monosemanticity
- feature-extraction
- vibe-thinker
- interpretability
- atlas
- dataset
- neural-network
- model-analysis
- layer-analysis
dataset_info:
  features:
    - name: layers
      description: per-layer metadata (model_id, n_prompts, corpus_hash, flags)
    - name: features
      description: per-component feature taxonomy and activation stats
    - name: per_head
      description: per-head selectivity and top-code stats
    - name: ov_circuits
      description: per-head OV/QK/FC SVD and spectral metrics
    - name: logit_lens
      description: top promoted/suppressed tokens per feature
    - name: coactivation
      description: feature-pair correlations and dominant buckets
    - name: code_analysis
      description: entangled vs selective role labels
    - name: compliance_behaviour_features
      description: authentic-vs-corporate F-stat and delta per feature
    - name: compliance_behaviour_per_head
      description: per-head compliance/behavior stats
    - name: subzero_layer
      description: per-layer Sub-Zero classifier and SV metadata
    - name: subzero_svs
      description: bouncer singular values per projection
    - name: subzero_capability
      description: DAS-axis capability damage and fence decisions
---

# `juiceb0xc0de/tmax-2b-atlas`

A brain atlas for **allenai/tmax-2b**, a hybrid SSM/Mamba/transformer language model. This is not a chat dataset or a benchmark — it is an internal-mechanics map of the model, built by running activations through a corpus of prompts and scoring what each layer, component, head, and feature direction is doing.

If you want to know where the model stores compliance style, which late-layer directions you can edit without breaking reasoning, or whether the hybrid attention heads are copy-paste circuits, this is the dataset.

## What was run

- **Model:** `allenai/tmax-2b`
- **Corpus:** 8,965 diverse prompts
- **Layers probed:** all 24
- **Attention layers:** 3, 7, 11, 15, 19, 23
- **Hybrid layers:** 0–2, 4–6, 8–10, 12–14, 16–18, 20–22
- **Passes:** activation census, feature taxonomy, per-head analysis, OV-circuit SVD, logit lens, coactivation, code-analysis, compliance-behavior contrast, Sub-Zero surgery with capability fence

## What the tables contain

| Table | Rows | What it gives you |
|---|---|---|
| `layers` | 24 | layer metadata and completion flags |
| `features` | 681,984 | feature taxonomy + activation stats per `(layer, component, feature_idx)` |
| `per_head` | 168 | per-head selectivity on the 6 attention layers |
| `ov_circuits` | 48 | SVD over `W_V @ W_O` plus QK/FC spectral metrics |
| `logit_lens` | 6,528 | promoted/suppressed output tokens per feature |
| `coactivation` | 14,201 | feature-pair correlations |
| `code_analysis` | 4,680 | `entangled` vs `selective` role labels |
| `compliance_behaviour_features` | 681,984 | authentic-vs-corporate contrast per feature |
| `compliance_behaviour_per_head` | 168 | per-head compliance/behavior contrast |
| `subzero_layer` | 24 | classifier accuracy and SV summary per layer |
| `subzero_svs` | 96 | bouncer singular values per projection |
| `subzero_capability` | 365 | DAS-axis damage and capability-fence decisions |

## Key findings

- **Attention is distributed, not memorized.** OV spectral concentration is **0.060** with effective rank ~79.
- **MLP/SSM gates dominate the logit lens.** Top logit-lens peaks are `gate` features in layers 15 and 20, with F-stats over 590.
- **The worst surgical surprise is early.** Layer 1 `linattn_in_proj_z` axis 0 fails the capability fence across all five domains, with up to **0.60 damage to multilingual**.
- **Otherwise editable.** 335 of 365 Sub-Zero axes pass the capability fence (91.8%). Average damage is 0.029.

## What Sub-Zero is measuring

The Sub-Zero pass is not a generic "find all important directions" sweep. It specifically looks for directions that separate corporate style from authentic style, then uses DAS rotation and a capability fence to check whether removing those directions damages code, math, reasoning, factual, or multilingual ability. The rows in `subzero_capability` are domain-by-domain damage scores for those compliance/behavior candidate axes, not a census of every load-bearing direction in the model.

## Important caveat

The hybrid SSM/Mamba layers contain components that do not tokenize language. I deliberately did not probe those components because the activations would just produce noise with no interpretable structure. This atlas covers the language-tokenizing components only. I am working on a method to capture whatever those hybrid layers are actually doing, but it is not included here yet.

## How to use

```python
import sqlite3
import pandas as pd

conn = sqlite3.connect("tmax-2b-atlas.sqlite")
df = pd.read_sql_query("SELECT * FROM features WHERE layer_id=15 AND component='gate' ORDER BY fstat DESC LIMIT 20", conn)
```

Or just browse the tables with any SQLite viewer.

## Backend environment (convenience only)

If you want to run Qwen3.5-family or tmax-family models yourself, the `backend/` folder contains the Dockerfile and GitHub Actions workflow I use to build a CUDA 12.8 / torch 2.7 image with prebuilt `flash-attn`, `causal-conv1d`, `mamba-ssm`, and `flash-linear-attention` wheels. It is provided as-is and is not the main attraction of this dataset.

## License

MIT.

## Contact / more

- Model: https://huggingface.co/allenai/tmax-2b
- Atlas code: https://github.com/JuiceB0xC0de/qwip_atlas (or point to your current repo)
- Follow: https://huggingface.co/juiceb0xc0de