---

name: models-and-inference description: "Route xTuring model selection, loading, saving, generation, and backend-specific inference choices." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

models-and-inference

Use this sub-skill when the user needs to pick a supported model key, load a saved checkpoint, tune generation settings, save a model, or choose between base, LoRA, INT8, and K-bit variants.

Use this for

• BaseModel.create("<key>") for supported registry-backed families.
• BaseModel.load(path) for saved xTuring checkpoints and bundled x/... hub entries.
• BaseModel.load(path, model_name="<key>") for a plain Hugging Face checkpoint directory without xturing.json.
• GenericModel and the other Generic* wrappers for arbitrary local or hub checkpoints.
• model.generate(...), model.save(...), and model.generation_config().

Route elsewhere for

• dataset schemas, dataset conversion, or self-instruct generation -> data-prep-and-generation
• fine-tuning, DPO, or trainer configuration -> training-and-alignment
• CLI chat, API server, or playground UI -> cli-api-ui
• perplexity or adapter-style evaluation -> evaluation

Read first

1. references/model-catalog.md
2. references/inference-workflows.md
3. references/troubleshooting.md
4. scripts/inspect_xturing_install.py

Known gap

• StableDiffusion is registered but placeholder-only; instantiating it raises NotImplementedError, so do not route users there as a usable inference target.

This sub-skill is self-contained; do not depend on the original source checkout for model-selection or inference guidance.