---
license: apache-2.0
language:
- en
library_name: sqlite
pretty_name: Qwen3.5-2B-Base Atlas
task_categories:
- feature-extraction
tags:
- mechanistic-interpretability
- interpretability
- activation-atlas
- feature-extraction
- atlas
- dataset
- neural-network
- model-analysis
- layer-analysis
- qwen3.5
- hybrid-architecture
- linear-attention
- base-model
size_categories:
- 100K<n<1M
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 statistics
  - name: compliance_behaviour_features
    description: authentic-vs-corporate F-stat, delta, and per-side means per feature
  - name: compliance_behaviour_per_head
    description: per-head authentic-vs-corporate separation on the six full-attention layers
  - name: per_head
    description: per-head selectivity on the six full-attention layers
  - name: ov_circuits
    description: per-head OV/QK SVD, spectral metrics, and induction scores
  - name: logit_lens
    description: top promoted/suppressed output tokens per feature
  - name: coactivation
    description: feature-pair correlations and dominant buckets
  - name: code_analysis
    description: entangled vs selective role labels
  - name: subzero_layer
    description: per-layer Sub-Zero classifier accuracy and SV metadata
  - name: subzero_svs
    description: retained Sub-Zero singular vectors per projection
  - name: subzero_capability
    description: DAS-axis capability damage and fence decisions
  - name: sae_features
    description: SAE feature table, empty for this run
---

# `juiceb0xc0de/Qwen3.5-2B-Base`

A brain atlas for **[Qwen/Qwen3.5-2B-Base](https://huggingface.co/Qwen/Qwen3.5-2B-Base)**, a 24-layer hybrid that runs linear attention on 18 layers and full attention on the other 6. This is not a chat dataset or a benchmark. It is an internal-mechanics map built by running activations through a corpus of prompts and scoring what each layer, component, head, and feature direction is doing.

This is a base model, before any instruction tuning, so whatever structure shows up here was put there by pretraining alone and anything built on these weights inherits it. If you want to know what a hybrid looks like when it only gets six attention layers to work with, where the model keeps its output vocabulary, or which directions in it are load-bearing, this is the dataset.

## What was run

- **Model:** `Qwen/Qwen3.5-2B-Base`
- **Corpus:** 8,965 diverse prompts across 17 buckets
- **Layers probed:** all 24
- **Full-attention layers:** 3, 7, 11, 15, 19, 23, with per-head capture (`attn`, `heads`, `q`, `k`, `v`)
- **Linear-attention layers:** the other 18, captured as `linattn_qkv`, `linattn_z`, and `linattn_out`
- **MLP components on every layer:** `mlp`, `gate`, `up`
- **Contrast:** authentic against corporate register
- **Passes:** activation census, feature taxonomy, per-head analysis, OV-circuit SVD, logit lens, coactivation, code-analysis, register contrast, Sub-Zero surgery with capability fence across code, math, reasoning, factual, and multilingual

## Architecture notes

| Property | Value |
|---|---|
| Hidden size | 2,048 |
| Layers | 24 (18 linear attention + 6 full attention) |
| Intermediate size | 6,144 |
| Full-attention interval | every 4 layers, at 3, 7, 11, 15, 19, 23 |
| Query heads | 8 |
| KV heads | 2 |
| GQA group size | 4 query heads per KV head |
| Head dimension | 256 |
| Attention output gate | enabled |
| Partial rotary factor | 0.25 |
| Linear-attention key heads / value heads | 16 / 16, both 128-dimensional |
| Linear-attention conv kernel | 4 |
| Vocabulary | 248,320 |
| Tied embeddings | yes |
| Max position embeddings | 262,144 (rope type `default`) |
| MTP layers | 1 (not probed) |
| Vision tower | 24 layers, 1,024-wide (not probed) |
| Training stage | base, no instruction tuning |

Three structural facts shape how the tables should be read.

**The full-attention layers are narrower than the linear ones.** A linear-attention layer carries six components and 28,672 coordinates; a full-attention layer carries eight and 27,648. Eight heads at 256 dimensions is 2,048, exactly the residual width, so the attention block contributes less captured surface than the linear-attention block it replaces. The `features` row count closes exactly against that split at 18 × 28,672 + 6 × 27,648 = **681,984**, and `head_idx // 4` equals `kv_head` on all 48 rows of `ov_circuits`.

The `q` component is 4,096 wide, twice the 2,048 that 8 heads at 256 would give. That is the attention output gate: `q_proj` emits the query and its gate concatenated. The per-head pass split that width by head dimension and produced 16 rows per layer for an 8-head model, so `per_head` holds 96 `q` rows across 6 layers where 48 would be right, and the upper 8 per layer are gate coordinates.

The linear-attention components pack differently. `linattn_qkv` is 6,144 wide, consistent with 16 query, 16 key, and 16 value heads at 128 dimensions each; `linattn_z` at 2,048 is the value-path gate; `linattn_out` at 2,048 is the projection back into the residual stream. These are the projections around the recurrence, not the recurrence itself.

## What the tables contain

| Table | Rows | What it gives you |
|---|---|---|
| `features` | 681,984 | feature taxonomy + activation stats per `(layer, component, feature_idx)` |
| `compliance_behaviour_features` | 681,984 | authentic-vs-corporate contrast per feature |
| `coactivation` | 13,461 | feature-pair correlations |
| `logit_lens` | 6,528 | promoted/suppressed output tokens per feature |
| `code_analysis` | 4,680 | `entangled` vs `selective` role labels |
| `subzero_capability` | 540 | 108 DAS axes × 5 capability domains |
| `subzero_svs` | 142 | retained bouncer singular vectors per projection |
| `compliance_behaviour_per_head` | 168 | per-head register separation |
| `per_head` | 168 | per-head selectivity |
| `ov_circuits` | 48 | 8 heads × 6 full-attention layers |
| `layers` | 24 | layer metadata and completion flags |
| `subzero_layer` | 24 | classifier accuracy and SV summary per layer |
| `sae_features` | **0** | not run |

## Key findings

### 1. The activation distribution has a populated middle

Across all **681,984** coordinates, activation rate spreads out rather than piling at the ends.

| Band | Coordinates | Share |
|---|---|---|
| Rate ≤ 0.05 | 186,388 | 27.33% |
| Rate 0.05 to 0.90 | 371,162 | **54.42%** |
| Rate ≥ 0.90 | 124,434 | 18.25% |

More than half the model sits in the context-dependent middle, where a coordinate fires on some prompts and not others. That is where selectivity can live, and it is the largest population.

The taxonomy says it with four labels: `partial_shared` at 30.76%, `non_activated` at 27.33%, `broadly_shared` at 23.65%, `all_shared` at 18.25%.

### 2. Two components carry all the gating behavior, and the gate is the specialist

Census statistics by component, sorted by how much of each sits quiet:

| Component | Activation rate | Mean activation | Quiet coords | Mean F-stat |
|---|---|---|---|---|
| `q` | 0.257 | **-1.1075** | **61.9%** | 42.8 |
| `gate` | **0.176** | -0.0804 | 60.0% | 50.2 |
| `linattn_z` | 0.563 | -0.0033 | 33.0% | 45.8 |
| `linattn_qkv` | 0.500 | 0.0020 | 27.3% | 41.7 |
| `up` | 0.501 | 0.0012 | 13.6% | 52.9 |
| `linattn_out` | 0.502 | -0.0003 | 11.8% | 46.0 |
| `mlp` | 0.500 | 0.0001 | **6.7%** | 36.8 |

Only `q` and `gate` show the pre-sigmoid signature of a gate: low firing rate and a strongly negative resting activation. `linattn_z` is nominally the value-path gate but does not behave like one here, sitting at a 0.563 firing rate with a resting activation of -0.0033 and 48.4% of its coordinates saturated. Whatever gating it performs is not visible as sparsity in this capture.

The gate is also the most selective component under code-analysis at **92.1% selective** against 61.1% for `k`, it holds **65 of the 85** domain-specific directions the taxonomy resolved anywhere in the model, and it is the strongest logit-lens component at a mean F-stat of **257.1**, running **1.72×** the `mlp` path and 2.08× `linattn_out`.

### 3. Two of the eight query heads do almost no induction

Induction across the 48 head-circuits averages **0.753**. Twelve circuits score below 0.5, and every one of them belongs to `kv_head` 0.

With only two KV groups, the split is stark:

| KV group | Mean induction |
|---|---|
| 0 | **0.594** |
| 1 | 0.913 |

Inside group 0 the split is by head index:

| Head | Mean induction | Range |
|---|---|---|
| 0 | **0.183** | 0.060 to 0.305 |
| 1 | 1.013 | 0.885 to 1.105 |
| 2 | **0.159** | 0.109 to 0.262 |
| 3 | 1.021 | 0.988 to 1.082 |

Heads 0 and 2 sit near the floor in all six attention layers. Heads 1 and 3, reading the same keys and values, sit near the top. The two populations never overlap at any layer.

The same alternation appears in group 1 but much gentler, at 0.854 for even heads against 0.972 for odd. In group 0 the gap is **0.85**.

The gentle even/odd alternation is small enough to be a property of how the metric interacts with head ordering. The collapse in group 0 is a different magnitude, reproduces in all six layers independently, and says the first KV group runs two heads doing something other than copy-and-continue while its other two do little else. Naming that something needs an ablation this atlas does not have.

### 4. Induction is modest and does not vary much by layer

| Layer | 3 | 7 | 11 | 15 | 19 | 23 |
|---|---|---|---|---|---|---|
| Mean induction | 0.786 | 0.766 | 0.735 | 0.762 | **0.722** | 0.749 |

The spread from highest to lowest layer is 0.064, and the trend across depth is a mild decline. Only **34 of 48** head-circuits score above 0.8.

With six attention layers rather than a full stack, each one carries comparable copy-and-continue work rather than the job being handed to a specialist pair. The 18 linear-attention layers are invisible to this measurement, so this describes how the attention budget is spent and not how sequence mixing divides across the model.

`induction_score` exceeds 1.0 on 10 of 48 head-circuits and peaks at **1.105**, so it is not normalized to a unit interval and should be read as a relative score.

### 5. The last attention layer's OV path collapses

| Layer | OV effective rank | OV concentration | QK effective rank |
|---|---|---|---|
| 3 | 90.6 | 0.041 | 30.5 |
| 7 | 80.9 | 0.057 | 46.1 |
| 11 | 96.3 | 0.050 | 32.5 |
| 15 | 82.9 | 0.049 | 25.8 |
| 19 | 86.9 | 0.030 | 20.8 |
| 23 | **38.4** | **0.130** | 23.0 |

Layer 23 runs its OV transform on **38.4** effective dimensions where every other attention layer uses between 80.9 and 96.3, and its spectral concentration is more than double the next highest. It is less than half the width of any other layer in the model on this measurement.

The QK path does not do the same thing. Layer 23 sits at 23.0, in line with layers 15 and 19, so the narrowing is specific to the value transform rather than to routing.

Across the whole table the OV path runs at **31.0%** of the 256-dimensional head and QK at **11.6%**. The final attention layer is doing a much lower-rank value transform than anything before it, which is what a layer that has stopped moving information around and started committing to an output looks like.

Effective rank does not transfer across architectures without normalizing by head dimension, so treat these as fractions rather than raw numbers.

### 6. Head redundancy is about the coordinate, not the group

In the `heads` component a feature index is `head*256 + d`, so an offset that is a multiple of 256 is the same within-head dimension in a different head, and a GQA group spans 1,024 coordinates.

The stored pairs separate the two factors:

| Pair type | Pairs | Mean correlation |
|---|---|---|
| Matched coordinate, same group | 791 | **0.920** |
| Unmatched coordinate, same group | 55 | 0.019 |
| Unmatched coordinate, cross group | 56 | **-0.275** |

Two heads in the same KV group, compared at the same within-head dimension, correlate at 0.920. Two coordinates in the same group that are *not* the same dimension sit at 0.019, essentially unrelated.

That is the control that matters. The redundancy is specific to matched coordinates and is not a general property of sharing a KV projection. Heads in a group run close to the same computation in the same coordinate frame.

The cross-group figure is negative at -0.275, but it rests on 56 pairs and should be read as directional rather than as a measured anti-correlation.

By component, `heads` is the most internally correlated at **0.791**, then `gate` at 0.727, `q` at 0.578, and `linattn_z` at 0.481, while everything else sits within a few hundredths of zero.

### 7. Register separation peaks at layer 8 and decays through the back half

Mean F-stat on the authentic-versus-corporate contrast, by layer:

| Layer | 0 | 4 | 6 | 8 | 11 | 15 | 19 | 23 |
|---|---|---|---|---|---|---|---|---|
| Mean F-stat | 22.1 | 85.4 | 104.9 | **106.7** | 71.4 | 56.7 | **51.9** | 60.3 |

Layer 0 is the floor at 22.1. Separation climbs by a factor of **4.8** to a peak of 106.7 at layer 8, then falls through the rest of the network to a low of 51.9 at layer 19 before a small recovery.

By component the attention family leads at the projections and trails at the output:

| Component | Mean F-stat | Max F-stat |
|---|---|---|
| `k` | **80.5** | 1,083.8 |
| `v` | 77.5 | 881.3 |
| `up` | 75.2 | 1,396.9 |
| `linattn_z` | 75.1 | 1,081.4 |
| `gate` | 66.5 | 1,356.0 |
| `mlp` | 66.4 | 1,139.3 |
| `heads` | 61.1 | 1,222.6 |
| `attn` | **61.1** | 1,053.3 |

The key and value projections separate the two registers at roughly **1.3×** the rate of the attention output. The single strongest direction is layer 6 `linattn_qkv` 1971 at **1,554.0**, and eight of the ten strongest sit in layers 4 through 9, the same band where the layer-wise average peaks.

Only two components carry a directional lean, and both are mild: `q` splits 53.6% authentic-leaning and `gate` 51.7%. Every other component sits within a point of even.

### 8. The register direction is decodable at 98.4% everywhere

`subzero_layer` fits a linear classifier for the register contrast at each of the 24 layers:

| Statistic | Value |
|---|---|
| Mean classifier accuracy | **0.984** |
| Range across 24 layers | 0.969 to **1.000** |

Accuracy is flat and high, and at least one layer separates the two conditions perfectly.

Set that against finding 7, where per-feature F-stat varies by a factor of nearly five across depth. The two do not conflict. A linear probe over the full residual width finds the register distinction at any layer; what changes with depth is how much of it has collected into individual coordinates.

If you want a named direction to edit, layers 4 through 9 hold the concentration. If you only want to detect register, any layer works.

### 9. The fence decision does not track damage

The surgery pass tested 108 DAS axes against five domains. 104 axes pass all five and 4 fail all five, with no axis splitting.

The two populations overlap on measured damage:

| Statistic | Value |
|---|---|
| Worst peak damage among passing axes | **0.1425** |
| Mildest peak damage among failing axes | **0.0368** |
| Passing axes with peak damage above that mildest failure | **15** |

Fifteen axes cleared the fence while doing more measured harm than an axis that was rejected. The worst passing axis, layer 12 `gate_proj` axis 0 at a peak of 0.1425, does roughly four times the damage of the mildest rejection.

`frozen` mirrors `fence_passed` exactly and adds no information, and no column in `subzero_capability` separates the groups. The atlas records the verdict without recording the criterion.

Damage is small throughout in absolute terms. The largest single measurement anywhere in the pass is **0.1748** nats per token, on layer 2 `gate_proj` axis 0, and mean damage across all 540 rows is 0.0144. By projection, `gate_proj` has the lowest pass rate at 84.2% and the highest mean damage at 0.0305; `up_proj`, `down_proj`, `linattn_out_proj`, and `linattn_in_proj_qkv` all pass at 100%.

Use `damage` to rank directions by measured harm and treat `fence_passed` on this run as a decision you cannot reconstruct. The failing population is 4 axes, which is thin enough that the pattern should be checked on a larger pass before it is leaned on.

### 10. The logit lens strengthens with depth, but F-stat does not predict readability

| Layers | 0-7 | 8-15 | 16-23 |
|---|---|---|---|
| Mean F-stat | 142.9 | 170.4 | **227.0** |

By component, `gate` leads at 257.1, then `up` at 198.6, `heads` at 159.2, `mlp` at 149.4, `linattn_out` at 123.6, and `attn` at 111.5.

Some features carry clean, coherent token families:

- Layer 18 `gate` 244 promotes ` classification`, ` recognition`, ` Classification`, ` classify`, ` Recognition`, ` classifications`, ` classifier`
- Layer 14 `up` 3830 promotes ` manual`, `Manual`, `manual`, ` manually`, `手动`, ` اليد`, ` manuale`, tying English, Chinese, Arabic, and Italian into one direction
- Layer 22 `gate` 2536 promotes ` cold`, ` chilling`, `冷`, ` Cold`, ` disturbed`, ` disturbing`, `黑暗`
- Layer 16 `gate` 888 promotes ` coefficients`, ` coefficient`, `coeff`, `Coeff`
- Layer 21 `gate` 3914 promotes ` author`, `历史和`, `历史`, `和历史`, `历史上的`, ` Histor`, ` ист`

Most of the top scorers are not like this. The single highest-scoring feature in the pass, layer 20 `gate` 257 at **555.2**, promotes ` Blanca`, ` circulation`, `kala`, `VT`, `èse`, `待`, ` ####` with no shared theme, and several features scoring above 450 are similarly unreadable.

The practical consequence is narrow. A high logit-lens F-stat on this model does not mean the direction is interpretable, so read the token list before treating anything here as a steering target. The pass is a source of candidates, not a ranked list of them.

## What Sub-Zero is measuring

The Sub-Zero pass is not a generic "find all important directions" sweep. It 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 candidate axes, not a census of every load-bearing direction in the model.

The candidate stage retained 142 singular vectors across 23 of the 24 layers, and 108 axes reached the fence. Finding 9 describes those 108 and nothing else.

## Important caveats

- **`fence_passed` cannot be reconstructed from the damage columns, and finding 9 is the evidence.** 15 axes cleared the fence while doing more measured harm than the mildest rejected axis. `frozen` mirrors `fence_passed` and adds nothing. Rank directions by `damage`; do not treat the pass/fail label as a damage threshold.
- **The failing population is 4 axes.** Every statistic about rejection in finding 9 rests on those four. The passing statistics are much better supported at 104.
- **142 singular vectors were retained but only 108 axes were fenced.** Thirty-four retained directions have no `subzero_capability` rows and were never damage-tested.
- **The SSM recurrence is not probed.** `linattn_qkv`, `linattn_z`, and `linattn_out` are the projections around the gated linear-attention path, not the recurrent state, the convolution, or the decay terms. On a model where 18 of 24 layers mix sequence information through that path, a large share of the machinery is outside what this atlas sees.
- **`per_head` reports 16 `q` heads for an 8-head model.** `q_proj` is 4,096 wide because the attention output gate concatenates the query and its gate. The upper 8 rows per layer are gate coordinates, not query heads.
- **`model_meta.json` is unpopulated.** Every geometry field in it is `null`. The architecture table was derived from the source model config and cross-checked against component widths in the database.
- **`ov_circuits.compliance_score` and `ov_circuits.layer_comp_strength` are NULL on all 48 rows.** They carry no information for this run.
- **`induction_score` is not bounded at 1.** It exceeds 1.0 on 10 of 48 head-circuits and peaks at 1.105.
- **`coactivation` stores a selected subset of feature pairs, not a full census.** Finding 6 rests on 902 head pairs, and both control cells hold only 55 and 56 pairs. Treat those contrasts as directional, particularly the negative cross-group figure. The redundancy result is correlational, a merging signal rather than proof that removal is free, which would need a fenced ablation run.
- **Only 85 domain-specific directions resolved** out of 681,984 features, 65 of them in `gate`, spread across layers 4 through 22. The corpus buckets are general-purpose categories. Read that as a statement about what a general-purpose corpus can see before reading it as a statement about the model.
- **`linattn_z` does not behave like a gate in this capture.** It carries a near-zero resting activation and a 0.563 firing rate. Whether that reflects the component's role or something about how it was captured is not answerable from this atlas alone.
- **Only six full-attention layers exist**, so findings 3, 4, 5, and 6 rest on 48 head-circuits total. Per-layer attention statistics have six points behind them.
- **Coactivation buckets describe the prompt mix.** Dominant buckets come out `business` at 18.2% and `introspection` at 8.6%, with 29.4% carrying no bucket at all. Those proportions reflect the corpus and the pair-selection step.
- **One behavioral axis only.** This run scored the authentic-versus-corporate register contrast. Nothing here speaks to content domain, refusal, or reasoning depth.
- **The vision tower and the MTP head are absent.** The config carries a 24-layer, 1,024-wide vision encoder and one multi-token-prediction layer. The text-only load path drops both.
- **The census aggregates per prompt, not per token.** Activation rate is the fraction of the 8,965 prompts on which a coordinate fires, so finding 1 is about prompt-level consistency rather than token-level sparsity.
- **Effective rank is not comparable across model families** without normalizing by head dimension. This model's heads are 256-dimensional.
- **Damage is in nats per token**, measured on the fence probes, not on any public benchmark.
- **No SAE features.** The `sae_features` table exists but is empty for this run.
- **No downstream benchmark is implied.** The atlas describes what the tensors do on this corpus, not whether the model is good at your task.

## How to use

`atlas.sqlite` is the primary query surface. `PRAGMA integrity_check` returns `ok`, and the `features` row count closes exactly against the model geometry at 18 linear-attention layers × 28,672 coordinates plus 6 full-attention layers × 27,648.

```python
import sqlite3
import pandas as pd

conn = sqlite3.connect("atlas.sqlite")

# only two components in here actually gate: which ones, and how hard?
df = pd.read_sql_query("""
    SELECT component,
           ROUND(AVG(activation_rate), 4) AS act_rate,
           ROUND(AVG(mean_act), 4)        AS resting,
           ROUND(100.0*AVG(activation_rate <= 0.05), 2) AS pct_quiet
    FROM features
    GROUP BY component
    ORDER BY act_rate
""", conn)
```

The per-layer JSON under `layers/` and the pooled summaries under `cross_layer/` mirror the same data if you would rather not open the database.

```sql
-- the last attention layer stops spreading its value transform
SELECT layer_id,
       ROUND(AVG(ov_eff_rank), 1)      AS ov_rank,
       ROUND(AVG(ov_spectral_conc), 3) AS ov_conc,
       ROUND(AVG(qk_eff_rank), 1)      AS qk_rank
FROM ov_circuits
GROUP BY layer_id
ORDER BY layer_id;
```

## License

Apache 2.0, matching the source model.

## Contact / more

- Model: https://huggingface.co/Qwen/Qwen3.5-2B-Base
- Atlas code: https://github.com/JuiceB0xC0de/qwip_atlas
- Follow: https://huggingface.co/juiceb0xc0de