---

name: neuron-view description: "Route BertViz neuron_view query/key neuron inspection workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

neuron-view

Use this sub-skill when a BertViz task needs the neuron view: per-head attention plus the query and key vectors that produced each attention score.

Route here when

• You need bertviz.neuron_view.show to render the interactive neuron view in a notebook, Colab, or saved HTML workflow.
• You need bertviz.neuron_view.get_attention to compute the serialized attention/query/key payload for validation, custom inspection, or debugging.
• You need BertViz's vendored modified Transformer classes from bertviz.transformers_neuron_view because ordinary Hugging Face models do not expose query/key vectors in the schema expected by neuron view.
• You need sentence-pair neuron-view slicing for BERT or RoBERTa, or you need to explain why GPT-2 and XLNet sentence pairs fail.
• You need an offline toy BERT validation path that avoids pretrained model downloads.

Do not route here when

• The user already has attention tensors and only wants head view or model view rendering; use ../attention-views/SKILL.md.
• The task is generic Transformers fine-tuning, training, checkpoint serving, generation, or deployment.
• The task asks for encoder-decoder, cross-attention, or tensor-only head/model view workflows; use ../attention-views/SKILL.md.
• The task requires interpreting attention as a causal explanation of model predictions; this skill only operates BertViz neuron-view mechanics.

Start with

• references/api-reference.md for signatures, model_type values, output schema, and token partition behavior.
• references/workflows.md for BERT, GPT-2, RoBERTa, toy validation, HTML export, and offline/cache-first recipes.
• references/model-compatibility.md for vendored class choices, sentence-pair support, optional dependencies, and CPU expectations.
• references/troubleshooting.md for common errors and recovery steps.
• scripts/validate_toy_bert_attention.py for no-network local validation of get_attention behavior.

Routing shortcuts

• Use show(...) for display/export; it calls get_attention(..., include_queries_and_keys=True) internally and injects the payload into BertViz HTML/JavaScript.
• Use get_attention(...) for assertions, schema inspection, and non-display workflows.
• Valid model_type strings are exactly bert, gpt2, xlnet, and roberta; they are not inferred from the model class.
• BERT and RoBERTa support sentence-pair partitions (all, aa, ab, ba, bb); GPT-2 sentence pairs raise ValueError; XLNet sentence pairs raise NotImplementedError.
• Keep core neuron-view validation CPU-local and no-network whenever possible. Prefer the bundled toy script before trying remote pretrained notebooks or uncached from_pretrained(...) calls.
• If the workflow does not need query/key vectors, route to ../attention-views/SKILL.md instead of forcing vendored neuron-view models.