---

name: "x-transformers" description: "Route x-transformers transformer construction, sequence wrappers, and recipe workflows through self-contained sub-skills." metadata: disco-role: operating disable-model-invocation: true license: MIT

x-transformers

Use this skill when the user asks about the x-transformers package, its transformer constructors, wrapper APIs, vision wrapper, or the repository's training recipes.

Quick start

• For a safe installation check, read installation.
• For a fast runtime smoke, run scripts/probe_backend.py and scripts/smoke_models.py.
• For exact constructor and wrapper signatures, read API reference.
• For feature-selection and compatibility questions, read compatibility and the relevant sub-skill reference.
• For task-specific failures, read troubleshooting.

Choose a route

core-models

Use this when the request is about building or tuning the base transformer stack: TransformerWrapper, XTransformer, ViTransformerWrapper, AttentionLayers, Attention, Encoder, Decoder, PrefixDecoder, CrossAttender, AttentionPool, and TransformerBlock.

Typical triggers:

• choosing attention flags or positional families
• debugging shape or mask errors in a constructor
• building text, seq2seq, or vision-wrapper models from scratch

sequence-workflows

Use this when the request is about wrapper and specialized-model workflows: autoregressive generation, continuous sequences, xVal mixed inputs, XL recurrence, belief-state or next-latent objectives, DPO, FreeTransformer, GPTVAE, NeoMLP, entropy tokenization, or XMLatentDecoder flows.

Typical triggers:

• generation and sampling APIs
• continuous or mixed discrete/continuous sequences
• latent or preference objectives
• memory or test-time-training wrappers

training-recipes

Use this when the request is about the repository's train_*.py examples, enwik8 data assumptions, or a safe smoke for recipe-style training.

Typical triggers:

• understanding which training script to run
• checking required extras and data files
• adapting a recipe to a smaller or safer smoke

Route selection rules

1. If the task names a base constructor, attention flag, positional family, pooling mode, or vision wrapper, start with core-models.
2. If the task names a wrapper, generation method, continuous/xVal/memory/latent objective, or preference objective, start with sequence-workflows.
3. If the task names a train_*.py script or enwik8 recipe, start with training-recipes.
4. If the task is only about install/import/runtime identity, begin with references/installation.md and scripts/probe_backend.py.

Notes for future agents

• The package has no dedicated end-user CLI entry point; most workflows are Python APIs or example scripts.
• Keep runtime instructions self-contained inside this skill tree.
• If you need a quick end-to-end smoke, prefer scripts/smoke_models.py or the copy-task smoke under sub-skills/training-recipes/scripts/ rather than the long native recipes.