# Neuron Explorer for Qwen3.5-4B: See every neuron at work, make targeted edits

> Source: <https://github.com/lyramakesmusic/neuron-explorer>
> Published: 2026-10-26 19:46:54+00:00

Steering-first neuron explorer for Qwen3.5-4B. Every MLP neuron in a layer is pushed up and down on a few prompts, every rollout is kept, and the explorer lays the results out over a 3D view of the model.

```
uv sync
uv run python explorer/server.py        # http://localhost:8791
```

Reads sweep runs from `runs/` and per-layer stats from `stats/`, both produced by the sweep below; the caches in
`explorer/cache/` ship with the repo. Loads Qwen3.5-4B locally (MPS, CUDA or CPU) for live
steered chat, lens, connections and live inference over your own text.

- **map** : the model's layers in perspective, attention and MLP per layer, one point per neuron. Hover shows a neuron's
steer lens and logit lens; click opens it; click a layer's label to view its sheet face-on.`writes` places
neurons with similar down-projection columns next to each other.
- **list** : units ranked by word-cluster mass against random-direction controls;`lens` searches the steer and logit
lenses for a token,`fires` runs your text through the model and filters neurons by activation.
- **neuron** : steered rollouts per prompt and dose, a three-column multi-turn steered chat (−, 0, +), lens and
connections, and a name.
- **embeddings** : UMAP of a layer's rollouts, coloured by neuron, prompt, dose or kind.
- **Claude** (fourth neuron-panel tab, or the right-edge handle): a chat running the`claude` CLI (installed and
logged in) with the explorer as an MCP server (`explorer/agent_mcp.py` ). It reads neurons, cones and lenses, runs the
live model (steering, activations, next-token logprobs, ablations), moves the map, list and highlights, and has file
tools and a shell in your home folder. Its prompt is`explorer/agent_prompt.md` plus`explorer/agent_methodology.md` , and`explorer/agent_prompt.local.md` when present (untracked, for your own additions);`EXPLORER_CLAUDE_MODEL` picks the model. The server answers only pages and tools on this machine.

Runs on a CUDA 13 GPU pod with the steering fork of vLLM ([RhizoNymph/vllm](https://github.com/RhizoNymph/vllm)).

```
bash sweep/setup_pod.sh                                   # fork, deps, model; writes /workspace/env.sh
python sweep/neuron_stats.py --layers 22 --tokens 2000000 --out stats/q35-4b
python sweep/steer_sweep.py --stats stats/q35-4b_L22.npz --design main_rel --dose-unit rel \
    --neurons all --random-controls 256 --run q35-4b-L22
sweep/run_depth.sh "4 9 13 17" 384 64                     # random-subset sweep across several layers
```

`main_rel` doses are fractions of the median residual norm leaving the layer: {who, snake, orange} × {−0.7, +0.5, +0.8}
and {free} × {+3}, plus one unsteered baseline per prompt.

| script | output | 
|---|---|
| `sweep/neuron_stats.py` | per-neuron activation extremes by sign regime, down columns, residual norms | 
| `sweep/steer_sweep.py` | `runs/<run>.jsonl` : text, token ids, logprobs, top-k per step; optional activation captures | 
| `sweep/rank.py` | `runs/<run>.units.jsonl` : distinctive words, cluster mass, percentile against controls | 
| `sweep/steer_lens.py` | `stats/steer_lens_L{l}.npz` : next-token Δlogprob and KL for ±0.8 pushes on every neuron | 
| `sweep/depth_report.py` | `runs/depth_report.json` : per-layer share of neurons above controls, coherence, top units | 
| `sweep/shift.py` | word-shift score against controls, all words counted | 
| `sweep/merge_runs.py` | joins sharded runs of one layer | 
| `sweep/check_equivalence.py` | HF vs vLLM steering and capture agreement | 
| `sweep/bench.py` ,`sweep/peek.py` | throughput conditions; terminal views of a run | 
| `explorer/build_embeddings.py` | `runs/<run>.umap.json` | 
| `explorer/build_layout.py` | `explorer/cache/layout_writes.npz` | 
| `explorer/build_connections.py` | `explorer/cache/connections.npz` : each neuron's 24 strongest direct-path links up and down | 
| `sweep/capture_prefill.py` | `captures/<run>.mlp_in.npy` : MLP input at the last prompt token at the run's capture layers, steered | 

`research_log.md` holds the findings and reasoning; `NOTES.md` the source notes.