Create README.md

README.md

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+---
+model: WeiboAI/VibeThinker-1.5B
+atlas_type: activation census + Sub-Zero brain atlas
+corpus: 9,523 diverse prompts
+layers: 28
+sacred_layers: 18-27
+---
+
+# VibeThinker-1.5B Brain Atlas
+
+This is an internal-mechanics atlas for the 1.5B parameter VibeThinker model. The goal was not to benchmark end-task accuracy, but to map what the network is actually doing with its parameters: where it computes, where it stores behaviorally relevant structure, and which late-layer directions are safe to touch.
+
+## What was run
+
+- **Activation census** over 9,523 prompts spanning compliance, reasoning, code, math, multilingual, and refusal-style questions.
+- **Per-layer feature taxonomy** for `mlp`, `gate`, `up`, and attention heads.
+- **OV-circuit spectral analysis** per head (`W_V @ W_O`).
+- **Sub-Zero surgery pass** on every layer, with a capability fence across `code`, `math`, `reasoning`, `factual`, and `multilingual` domains.
+- Pipeline was run on a CPU-only environment.
+
+## Key geometry
+
+| Property | Value |
+|---|---|
+| Layers | 28 |
+| d_model | 1536 |
+| d_mlp | 8960 |
+| Attention heads | 12 |
+| KV heads | 2 |
+| Head dim | 128 |
+| Sacred (deep Sub-Zero) layers | 18–27 |
+
+## What the numbers suggest
+
+### The model is not a lookup table
+
+OV-circuit spectral concentration averages **0.049**, with effective rank around **55**. That is a distributed signature, not a sparse “copy-paste” attention pattern. Attention heads appear to be doing weighted computation across many directions, not memorizing specific token-to-token jumps.
+
+### Feature activation is broad
+
+The feature taxonomy is dominated by `partial_shared` and `broadly_shared` classes, with a smaller `non_activated` tail and very few `all_shared` features. Most dimensions responded to many prompts rather than one hyper-specific trigger.
+
+### Late layers are load-bearing
+
+Sub-Zero finds structured singular-value subspace only in layers 18–27, which is **36% of the network depth**. The first half of the model looks like wide preprocessing; the second half does the structured transformation.
+
+### Surgical fragility is the main caveat
+
+The capability fence keeps about **74.5%** of tested axes, but the rejected ones hit hard:
+
+- Layer 18 `up_proj` axis 0 does **0.81** damage to code generation.
+- Layer 18 `up_proj` axis 0 also scores the highest math, reasoning, and multilingual damage.
+- Several `down_proj` and `gate_proj` axes in the early sacred layers fail the fence.
+
+Interpretation: the 1.5B late-layer subspace is doing a lot of work per direction. It has less redundancy than the larger variant, so removing a top singular value tends to break more than one capability at once.
+
+### Classifier stability dips in the middle
+
+Sub-Zero classifier accuracy drops to **0.75–0.83** around layers 13–17, then recovers in the late sacred layers. That mid-network region is messier or more entangled than the clean late-layer representation.
+
+## Bottom line
+
+VibeThinker-1.5B behaves like a compact reasoning model: distributed attention, broad-feature MLPs, and a deep-but-narrow sacred region where a small number of directions carry most of the task load. It is interpretable, but not easy to edit safely because its late layers are not highly redundant.