---

name: "sequence-workflows" description: "Routes x-transformers wrapper, generation, continuous, latent, memory, preference, and specialized sequence workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

sequence-workflows

Use this sub-skill when the task is to choose or operate a higher-level x-transformers wrapper or specialized sequence model rather than designing attention layers from scratch.

Use this route for

• Autoregressive text generation, beam search, prompt lists, forgetful causal-mask training, prepended embeddings, or cached decoding.
• Non-autoregressive masked-token generation with iterative demasking and optional token critics.
• Continuous sequence models, continuous autoregressive generation, and continuous autoencoding.
• Multi-input token streams where several categorical features are embedded and summed at each position.
• xVal mixed discrete-token and continuous-number modeling.
• Transformer-XL-style segmented recurrence, memories, episodic memories, and test-time-training wrapper flows.
• Belief-state, next-latent, LEJEPA-style latent autoregressive, XM latent-decoder, FreeTransformer, GPTVAE, NeoMLP, DPO, and entropy-based tokenization workflows.

Do not use this route for

• Selecting attention flags, positional-bias families, normalization, residual, cross-attention, or low-level Attention construction. Use core-models instead.
• Running or modifying repository train_* recipes. Use training-recipes instead.
• Importing skills into another agent runtime.

Read first

• references/wrapper-contracts.md for wrapper signatures, shape contracts, generation semantics, and model-selection guidance.
• references/troubleshooting.md for predictable assertion failures and recovery steps.

Operating rules

1. Pick the smallest wrapper that matches the data type: token, continuous, mixed token/number, multi-input token, recurrent-memory, latent, or preference-pair.
2. Build the underlying TransformerWrapper, ContinuousTransformerWrapper, XValTransformerWrapper, encoder, or decoder with core-models guidance, then return here for wrapper-specific calls.
3. Keep tensor devices consistent. CPU examples are safe; CUDA can be used when all model inputs, masks, latents, memories, and start tokens are on the same device.
4. Treat seq_len carefully: most autoregressive wrappers interpret it as the number of new tokens, while FreeTransformer.generate interprets it as the total output length.
5. Before debugging internals, check shape equality constraints in references/wrapper-contracts.md and known symptoms in references/troubleshooting.md.

Cross-links inside this skill tree

• For base model construction and attention-feature compatibility, switch to core-models.
• For long-running script recipes and dataset prerequisites, switch to training-recipes.